Evidence map›Paper›PMID 33617402›Full record

ArticleAssistive technology : the official journal of RESNA2022

Validating a wheelchair in-seat activity tracker.

Nauman Ahad, Sharon E Sonenblum, Mark A Davenport, Stephen Sprigle

Open access · greenAbstract read
In one paragraph

Article in Assistive technology : the official journal of RESNA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
5.3field-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

10 citing papers in PubMed, 16 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Nauman AhadSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.ORCID 0000-0002-6475-8334
Sharon E SonenblumRehabilitation Engineering and Applied Research Lab, Georgia Institute of Technology, Atlanta, Georgia, USA.ORCID 0000-0001-9747-5683
Mark A DavenportSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.ORCID 0000-0001-6079-6328
Stephen SprigleRehabilitation Engineering and Applied Research Lab, Georgia Institute of Technology, Atlanta, Georgia, USA.ORCID 0000-0003-0462-0138
Georgia Institute of Technology · US

Funding

Development of Technologies to Increase In-Seat Movement to Prevent Sitting-Acquired Pressure Injuries in Wheelchair UsersR01AG056255 · NIA · MAYO CLINIC ROCHESTER · PI HALLBECK, M. SUSAN, MORROW, MELISSA M. · 2017 to 2021
$3.2M
NIA NIH HHS R01 AG056255
6 · The paper itself

Abstract

Wheelchair users often experience prolonged periods of stationary sitting. Such periods are accompanied with increased loading of the ischial tuberosities. This can lead to the development of pressure ulcers which can cause complications such as sepsis. Periodic pressure offloading is recommended to reduce the onset of pressure ulcers. Experts recommend the periodic execution of different movements to provide the needed pressure offloading. Wheelchair users, however, might not remember to perform these recommended movements in terms of both quality and quantity. A system that can detect such movements could provide valuable feedback to both wheelchair users as well as clinicians. The objective of this study was to present and validate the WiSAT - a system for characterizing in-seat activity for wheelchair users. WiSAT is designed to detect two kinds of movements - weight shifts and in-seat movements. Weight shifts are movements that offload pressure on ischial tuberosities by 30% as compared to upright sitting and are maintained for 15 seconds. In-seat movements are shorter transient movements that involve either a change in the center of pressure on the sitting buttocks or a transient reduction in total load by 30%. This study validates the use of WiSAT in manual wheelchairs. WiSAT has a sensor mat which was inserted beneath a wheelchair cushion. Readings from these sensors were used by WiSAT algorithms to predict weight shifts and in-seat movements. These weight shifts and in-seat movements were validated against a high-resolution interface pressure mat in a dataset that resembles real-world usage. The proposed system achieved weight shift precision and recall scores of 81% and 80%, respectively, while in-seat movement scores were predicted with a mean absolute error of 22%. Results showed that WiSAT provides sufficient accuracy in characterizing in-seat activity in terms of weight shifts and in-seat movement.

Indexed as

Pressure UlcerWheelchairsButtocksFitness TrackersHumansPressureelectronic aids to daily livingemerging trendsinformation technology and telecommunicationsinstrumentation

Identifiers

PMID33617402
PMCPMC8492781
OpenAlexW3130426452

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.