Evidence map›Paper›PMID 27527172›Full record

ArticleSensors (Basel, Switzerland)2016

Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes.

Christopher Moufawad El Achkar, Constanze Lenoble-Hoskovec, Anisoara Paraschiv-Ionescu, Kristof Major, Christophe Büla, Kamiar Aminian

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
15.6field-weighted citation impact, top 1% 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.

  1. Pooled it
  2. Trial
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  8. Article
  9. The Effects of Insole-Based Visual Feedback on Weight-Bearing in Patients Undergoing Total Hip Replacement.International journal of environmental research and public health · 2021
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  16. [Mobility in older people].Zeitschrift fur Gerontologie und Geriatrie · 2019
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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

6 authors at 2 institutions in 1 country.

Christopher Moufawad El AchkarLaboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. christopher.moufawadelachkar@epfl.ch.
Constanze Lenoble-HoskovecCentre Hospitalier Universitaire Vaudois (CHUV), Service de gériatrie et réadaptation gériatrique, 1011 Lausanne, Switzerland. constanze.hoskovec@chuv.ch.
Anisoara Paraschiv-IonescuLaboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. anisoara.ionescu@epfl.ch.
Kristof MajorCentre Hospitalier Universitaire Vaudois (CHUV), Service de gériatrie et réadaptation gériatrique, 1011 Lausanne, Switzerland. kristof.major@chuv.ch.
Christophe BülaCentre Hospitalier Universitaire Vaudois (CHUV), Service de gériatrie et réadaptation gériatrique, 1011 Lausanne, Switzerland. christophe.bula@chuv.ch.
Kamiar AminianLaboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. kamiar.aminian@epfl.ch.
École Polytechnique Fédérale de Lausanne · CHUniversity Hospital of Lausanne · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Activity level and gait parameters during daily life are important indicators for clinicians because they can provide critical insights into modifications of mobility and function over time. Wearable activity monitoring has been gaining momentum in daily life health assessment. Consequently, this study seeks to validate an algorithm for the classification of daily life activities and to provide a detailed gait analysis in older adults. A system consisting of an inertial sensor combined with a pressure sensing insole has been developed. Using an algorithm that we previously validated during a semi structured protocol, activities in 10 healthy elderly participants were recorded and compared to a wearable reference system over a 4 h recording period at home. Detailed gait parameters were calculated from inertial sensors. Dynamics of physical behavior were characterized using barcodes that express the measure of behavioral complexity. Activity classification based on the algorithm led to a 93% accuracy in classifying basic activities of daily life, i.e., sitting, standing, and walking. Gait analysis emphasizes the importance of metrics such as foot clearance in daily life assessment. Results also underline that measures of physical behavior and gait performance are complementary, especially since gait parameters were not correlated to complexity. Participants gave positive feedback regarding the use of the instrumented shoes. These results extend previous observations in showing the concurrent validity of the instrumented shoes compared to a body-worn reference system for daily-life physical behavior monitoring in older adults.

Indexed as

ShoesActivities of Daily LivingAgedGaitHumansMonitoring, PhysiologicWalkingactivity classificationbehavioral complexitygait analysisinertial measurement unitpressure insolewearable sensors

Identifiers

PMID27527172
PMCPMC5017390
OpenAlexW2493846716

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.