Evidence map›Paper›PMID 26390946›Full record

ArticleComputer methods and programs in biomedicine2015

Assessing interactions among multiple physiological systems during walking outside a laboratory: An Android based gait monitor.

E Sejdić, A Millecamps, J Teoli, M A Rothfuss, N G Franconi, S Perera, A K Jones, J S Brach, M H Mickle

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computer methods and programs in biomedicine, 2015. 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
6.4field-weighted citation impact, top 5% 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

2 citing papers in PubMed, 15 citations in OpenAlex.

  1. Review
  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

9 authors at 1 institution in 1 country.

E SejdićDepartment of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: esejdic@ieee.org.
A MillecampsDepartment of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
J TeoliDepartment of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
M A RothfussDepartment of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
N G FranconiDepartment of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
S PereraDivision of Geriatric Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
A K JonesDepartment of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
J S BrachDepartment of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, USA.
M H MickleDepartment of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
University of Pittsburgh · US

Funding

VARIABILITY, STABILITY & SMOOTHNESS OF WALKINGP30AG024827 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI JENNIFER S BRACH · 2004 to 2026
$28.6M
NIA NIH HHS P30 AG024827
6 · The paper itself

Abstract

Gait function is traditionally assessed using well-lit, unobstructed walkways with minimal distractions. In patients with subclinical physiological abnormalities, these conditions may not provide enough stress on their ability to adapt to walking. The introduction of challenging walking conditions in gait can induce responses in physiological systems in addition to the locomotor system. There is a need for a device that is capable of monitoring multiple physiological systems in various walking conditions. To address this need, an Android-based gait-monitoring device was developed that enabled the recording of a patient's physiological systems during walking. The gait-monitoring device was tested during self-regulated overground walking sessions of fifteen healthy subjects that included 6 females and 9 males aged 18-35 years. The gait-monitoring device measures the patient's stride interval, acceleration, electrocardiogram, skin conductance and respiratory rate. The data is stored on an Android phone and is analyzed offline through the extraction of features in the time, frequency and time-frequency domains. The analysis of the data depicted multisystem physiological interactions during overground walking in healthy subjects. These interactions included locomotion-electrodermal, locomotion-respiratory and cardiolocomotion couplings. The current results depicting strong interactions between the locomotion system and the other considered systems (i.e., electrodermal, respiratory and cardiovascular systems) warrant further investigation into multisystem interactions during walking, particularly in challenging walking conditions with older adults.

Indexed as

AdolescentAdultElectrocardiographyFemaleGaitHumansMaleMonitoring, PhysiologicRespirationSoftware DesignWalkingYoung AdultElectrocardiogramGait accelerometry signalsGait monitoringRespiratory rateSkin conductanceStride intervals

Identifiers

PMID26390946
PMCPMC4648697
OpenAlexW1819536205

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.