Evidence map›Paper›PMID 32636006›Full record

ArticleJournal of biomechanics2020

On the application of entropic half-life and statistical persistence decay for quantification of time dependency in human gait.

Peter C Raffalt, Jennifer M Yentes

Abstract read
In one paragraph

Article in Journal of biomechanics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Peter C RaffaltDepartment of Physical Performance, Norwegian School of Sport Sciences, Sognsveien 220, 0806 Oslo, Norway. Electronic address: peter.raffalt@nih.no.
Jennifer M YentesCenter for Research in Human Movement Variability, Department of Biomechanics, University of Nebraska Omaha, Omaha, NE, USA.

Funding

Visual control of locomotion in people with Parkinsons diseaseP20GM109090 · NIGMS · UNIVERSITY OF NEBRASKA OMAHA · PI MANGALAM, MADHUR · 2014 to 2023
$20.5M
Improving exercise capacity in chronic obstructive pulmonary disease patients through uphill walkingI21RX003294 · VA · OMAHA VA MEDICAL CENTER · PI ROMBERGER, DEBRA J · 2020 to 2022
–
NHLBI NIH HHS L30 HL129255NIGMS NIH HHS P20 GM109090RRD VA I21 RX003294
6 · The paper itself

Abstract

Entropic half-life (ENT½) and statistical persistence decay (SPD) was recently introduced as measures of time dependency in stride time intervals during walking. The present study investigated the effect of data length on ENT½ and SPD and additionally applied these measures to stride length and stride speed intervals. First, stride times were collected from subjects during one hour of treadmill walking. ENT½ and SPD were calculated from a range of stride numbers between 250 and 2500. Secondly, stride times, stride lengths and stride speeds were collected from subjects during 16 min of treadmill walking. ENT½ and SPD were calculated from the stride times, stride lengths and stride speeds. The ENT½ values reached a plateau between 1000 and 2500 strides whereas the SPD increased linearly with the number of included strides. This suggests that ENT½ can be compared if 1000 strides or more are included, but only SPD obtained from same number of strides should be compared. The ENT½ and SPD of the stride times were significantly longer compared to that of the stride lengths and stride speeds. This indicates that the time dependency is greater in the motor control of stride time compared to that of stride lengths and stride speeds.

Indexed as

GaitWalkingEntropyExercise TestHalf-LifeHumansDynamicsNonlinear analysisStride characteristicsTreadmill walkingVariability

Identifiers

PMID32636006
PMCPMC7871423

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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.