Evidence map›Paper›PMID 36440422›Full record

ArticleFrontiers in psychiatry2022

Real-time location systems technology in the care of older adults with cognitive impairment living in residential care: A scoping review.

Lynn Haslam-Larmer, Leia Shum, Charlene H Chu, Kathy McGilton, Caitlin McArthur, Alastair J Flint, Shehroz Khan, Andrea Iaboni

Open access · goldAbstract readScoping Review
In one paragraph

Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 17% 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

5 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Deep Learning and Geriatric Mental Health.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2024
    Review
  5. 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

8 authors at 4 institutions in 1 country.

Lynn Haslam-LarmerKITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
Leia ShumKITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
Charlene H ChuKITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
Kathy McGiltonKITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
Caitlin McArthurSchool of Physiotherapy, Dalhousie University, Halifax, NS, Canada.
Alastair J FlintDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Shehroz KhanKITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
Andrea IaboniKITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
University Health Network · CAUniversity of Toronto · CADalhousie University · CAToronto Rehabilitation Institute · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: There has been growing interest in using real-time location systems (RTLS) in residential care settings. This technology has clinical applications for locating residents within a care unit and as a nurse call system, and can also be used to gather information about movement, location, and activity over time. RTLS thus provides health data to track markers of health and wellbeing and augment healthcare decisions. To date, no reviews have examined the potential use of RTLS data in caring for older adults with cognitive impairment living in a residential care setting. Objective: This scoping review aims to explore the use of data from real-time locating systems (RTLS) technology to inform clinical measures and augment healthcare decision-making in the care of older adults with cognitive impairment who live in residential care settings. Methods: Embase (Ovid), CINAHL (EBSCO), APA PsycINFO (Ovid) and IEEE Xplore databases were searched for published English-language articles that reported the results of studies that investigated RTLS technologies in persons aged 50 years or older with cognitive impairment who were living in a residential care setting. Included studies were summarized, compared and synthesized according to the study outcomes. Results: A total of 27 studies were included. RTLS data were used to assess activity levels, characterization of wandering, cognition, social interaction, and to monitor a resident's health and wellbeing. These RTLS-based measures were not consistently validated against clinical measurements or clinically important outcomes, and no studies have examined their effectiveness or impact on decision-making. Conclusion: This scoping review describes how data from RTLS technology has been used to support clinical care of older adults with dementia. Research efforts have progressed from using the data to track activity levels to, most recently, using the data to inform clinical decision-making and as a predictor of delirium. Future studies are needed to validate RTLS-based health indices and examine how these indices can be used to inform decision-making.

Indexed as

cognitive impairmentdementialong-term careolder adultsremote monitoringresidential careRTLS (Real-time location system)wearable technological device

Identifiers

PMID36440422
PMCPMC9685159
OpenAlexW4308877343

What Socratic 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 Socratic graph.