Evidence map›Paper›PMID 32153420›Full record

ArticleFrontiers in physiology2020

Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments.

David Renggli, Christina Graf, Nikolaos Tachatos, Navrag Singh, Mirko Meboldt, William R Taylor, Lennart Stieglitz, Marianne Schmid Daners

Registry-linked trialAbstract read
In one paragraph

Article in Frontiers in physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04702035 (Walking Pattern Characteristics in Normal Pressure Hydrocephalus. A Phase I/II Open-label Single Center Trial to Characterize Disease-specific Walking Patterns Using Wearable Gyroscopes), which is not on this map. Cited by 48 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 2 pooled it
–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.

NCT04702035 nacompletednot on this map

Walking Pattern Characteristics in Normal Pressure Hydrocephalus. A Phase I/II Open-label Single Center Trial to Characterize Disease-specific Walking Patterns Using Wearable Gyroscopes

TypeinterventionalSponsorUniversity of ZurichRan2018 to 2024Enrolled60ConditionsIdiopathic Normal Pressure Hydrocephalus (INPH)ArmsCharacterization of walking pattern characteristics in an ambulatory setting using wearable IMUs (ZurichMOVE sensors)
3 · Its place in the literature

Who cites it

48 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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.

David RenggliProduct Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland.
Christina GrafProduct Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland.
Nikolaos TachatosProduct Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland.
Navrag SinghInstitute for Biomechanics, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
Mirko MeboldtProduct Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland.
William R TaylorInstitute for Biomechanics, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
Lennart StieglitzDepartment of Neurosurgery, University Hospital Zurich, Zurich, Switzerland.
Marianne Schmid DanersProduct Development Group Zurich, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWalking patterns can provide important indications of a person's health status and be beneficial in the early diagnosis of individuals with a potential walking disorder. For appropriate gait analysis, it is critical that natural functional walking characteristics are captured, rather than those experienced in artificial or observed settings. To better understand the extent to which setting influences gait patterns, and particularly whether observation plays a varying role on subjects of different ages, the current study investigates to what extent people walk differently in lab versus real-world environments and whether age dependencies exist.

methodsThe walking patterns of 20 young and 20 elderly healthy subjects were recorded with five wearable inertial measurement units (ZurichMOVE sensors) attached to both ankles, both wrists and the chest. An automated detection process based on dynamic time warping was developed to efficiently identify the relevant sequences. From the ZurichMOVE recordings, 15 spatio-temporal gait parameters were extracted, analyzed and compared between motion patterns captured in a controlled lab environment (10 m walking test) and the non-controlled ecologically valid real-world environment (72 h recording) in both groups.

resultsSeveral parameters (

conclusionElderly test subjects walked differently in controlled lab settings compared to their real-world environments, which indicates the need to better understand natural walking patterns under ecologically valid conditions before clinically relevant conclusions can be drawn on a subject's functional status. Moreover, the greater inter-group differences in real-world environments seem promising regarding the sensitive identification of subjects with indications of a walking disorder.

Indexed as

gait analysishydrocephalusIMU sensorsnatural walking patternsnon-controlled settingsreal-world environmentwalking disorderZurichMOVE

Identifiers

PMID32153420
PMCPMC7044412

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Registered trials

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.