Evidence map›Paper›PMID 38748680›Full record

ArticlePloS one2024

Quality in care homes: How wearable devices and social network analysis might help.

Carl Thompson, Adam Gordon, Kishwer Khaliq, Amrit Daffu-O'Reilly, Thomas Willis, Catherine Noakes, Karen Spilsbury

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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.

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Carl ThompsonSchool of Healthcare, University of Leeds, Leeds, West Yorkshire, United Kingdom.ORCID 0000-0002-9369-1204
Adam GordonDivision of Medical Sciences and Graduate Entry Medicine, University of Nottingham, Derby, Derbyshire, United Kingdom.
Kishwer KhaliqSchool of Civil Engineering, University of Leeds, Leeds, West Yorkshire, United Kingdom.ORCID 0000-0003-3582-9313
Amrit Daffu-O'ReillySchool of Healthcare, University of Leeds, Leeds, West Yorkshire, United Kingdom.
Thomas WillisLeeds Institute of Clinical Trials Research, University of Leeds, Leeds, West Yorkshire, United Kingdom.ORCID 0000-0002-0252-9923
Catherine NoakesSchool of Civil Engineering, University of Leeds, Leeds, West Yorkshire, United Kingdom.
Karen SpilsburySchool of Healthcare, University of Leeds, Leeds, West Yorkshire, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social network analysis can support quality improvement in care homes but traditional approaches to social network analysis are not always feasible in care homes. Recalling contacts and movements in a home is difficult for residents and staff and documentary and other sources of individual contacts can be unreliable. Bluetooth enabled wearable devices are a potential means of generating reliable, trustworthy, social network data in care home communities. In this paper, we explore the empirical, theoretical and real-world potential and difficulties in using Bluetooth enabled wearables with residents and staff in care homes for quality improvement. We demonstrate, for the first time, that a relatively simple system built around the Internet of Things, Bluetooth enabled wearables for residents and staff and passive location devices (the CONTACT intervention) can capture social networks and data in homes, enabling social network analysis, measures, statistics and visualisations. Unexpected variations in social network measures and patterns are surfaced, alongside "uncomfortable" information concerning staff time spent with residents. We show how technology might also help identify those most in need of social contact in a home. The possibilities of technology-enabled social network analysis must be balanced against the implementation-related challenges associated with introducing innovations in complex social systems such as care homes. Behavioural challenges notwithstanding, we argue that armed with social network information, care home staff could better tailor, plan and evaluate the effects of quality improvement with the sub-communities that make up a care home community.

Indexed as

Nursing HomesWearable Electronic DevicesHumansQuality ImprovementQuality of Health CareSocial Network Analysis

Identifiers

PMID38748680
PMCPMC11095701

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.