Evidence map›Paper›PMID 33195143›Full record

ArticleFrontiers in bioengineering and biotechnology2020

Muscle Activation Patterns Are More Constrained and Regular in Treadmill Than in Overground Human Locomotion.

Ilaria Mileti, Aurora Serra, Nerses Wolf, Victor Munoz-Martel, Antonis Ekizos, Eduardo Palermo, Adamantios Arampatzis, Alessandro Santuz

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

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  20. Evaluation of movement and brain activity.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2021
    Review
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.

Ilaria MiletiDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, Rome, Italy.
Aurora SerraDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, Rome, Italy.
Nerses WolfDepartment of Electrical Engineering and Informatics, Technische Universität Berlin, Berlin, Germany.
Victor Munoz-MartelDepartment of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany.
Antonis EkizosDepartment of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany.
Eduardo PalermoDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, Rome, Italy.
Adamantios ArampatzisDepartment of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany.
Alessandro SantuzDepartment of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of motorized treadmills as convenient tools for the study of locomotion has been in vogue for many decades. However, despite the widespread presence of these devices in many scientific and clinical environments, a full consensus on their validity to faithfully substitute free overground locomotion is still missing. Specifically, little information is available on whether and how the neural control of movement is affected when humans walk and run on a treadmill as compared to overground. Here, we made use of linear and non-linear analysis tools to extract information from electromyographic recordings during walking and running overground, and on an instrumented treadmill. We extracted synergistic activation patterns from the muscles of the lower limb

Indexed as

fractal analysislocomotionmotor controlmuscle synergiesoverground locomotiontreadmill locomotion

Identifiers

PMID33195143
PMCPMC7644811

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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.