Evidence map›Paper›PMID 24406708›Full record

SynthesisJournal of geriatric physical therapy (2001)

Fall detection devices and their use with older adults: a systematic review.

Shomir Chaudhuri, Hilaire Thompson, George Demiris

Open access · greenAbstract readSystematic Review
In one paragraph

Synthesis in Journal of geriatric physical therapy (2001). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed, 3 pooled it
12.6field-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

60 citing papers in PubMed, 3 syntheses or guidelines pooled it, 209 citations in OpenAlex.

  1. Pooled it
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  3. Predicting geriatric falls following an episode of emergency department care: a systematic review.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2014
    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

3 authors at 1 institution in 1 country.

Shomir Chaudhuri1Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle. 2Department of Biobehavioral Nursing and Health, University of Washington School of Nursing, Seattle.
Hilaire Thompson
George Demiris
University of Washington · US

Funding

University of Washington Biomedical Informatics and Data Science Research Training ProgramT15LM007442 · NLM · UNIVERSITY OF WASHINGTON · PI JOHN H. GENNARI, Andrea L. Hartzler · 2002 to 2026
$19.6M
NLM NIH HHS 2T15LM007442-11NLM NIH HHS T15 LM007442
6 · The paper itself

Abstract

backgroundFalls represent a significant threat to the health and independence of adults aged 65 years and older. As a wide variety and large number of passive monitoring systems are currently and increasingly available to detect when individuals have fallen, there is a need to analyze and synthesize the evidence regarding their ability to accurately detect falls to determine which systems are most effective.

objectivesThe purpose of this literature review is to systematically assess the current state of design and implementation of fall-detection devices. This review also examines to what extent these devices have been tested in the real world as well as the acceptability of these devices to older adults. DATA SOURCES: A systematic literature review was conducted in PubMed, CINAHL, EMBASE, and PsycINFO from their respective inception dates to June 25, 2013. STUDY ELIGIBILITY CRITERIA AND

interventionsArticles were included if they discussed a project or multiple projects involving a system with the purpose of detecting a fall in adults. It was not a requirement for inclusion in this review that the system targets persons older than 65 years. Articles were excluded if they were not written in English or if they looked at fall risk, fall detection in children, fall prevention, or a personal emergency response device. STUDY APPRAISAL AND SYNTHESIS

methodsStudies were initially divided into those using sensitivity, specificity, or accuracy in their evaluation methods and those using other methods to evaluate their devices. Studies were further classified into wearable devices and nonwearable devices. Studies were appraised for inclusion of older adults in sample and if evaluation included real-world settings.

resultsThis review identified 57 projects that used wearable systems and 35 projects using nonwearable systems, regardless of evaluation technique. Nonwearable systems included cameras, motion sensors, microphones, and floor sensors. Of the projects examining wearable systems, only 7.1% reported monitoring older adults in a real-world setting. There were no studies of nonwearable devices that used older adults as subjects in either a laboratory or a real-world setting. In general, older adults appear to be interested in using such devices although they express concerns over privacy and understanding exactly what the device is doing at specific times. LIMITATIONS: This systematic review was limited to articles written in English and did not include gray literature. Manual paper screening and review processes may have been subject to interpretive bias. CONCLUSIONS AND IMPLICATIONS OF KEY

findingsThere exists a large body of work describing various fall-detection devices. The challenge in this area is to create highly accurate unobtrusive devices. From this review it appears that the technology is becoming more able to accomplish such a task. There is a need now for more real-world tests as well as standardization of the evaluation of these devices.

Indexed as

Accidental FallsAgedEquipment DesignHumansMonitoring, AmbulatorySensitivity and SpecificityTelemedicine

Identifiers

PMID24406708
PMCPMC4087103
OpenAlexW2140943894

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.