Evidence map›Paper›PMID 38781012›Full record

ArticleJMIR nursing2024

The Cooperation Between Nurses and a New Digital Colleague "AI-Driven Lifestyle Monitoring" in Long-Term Care for Older Adults: Viewpoint.

Sjors Groeneveld, Gaya Bin Noon, Marjolein E M den Ouden, Harmieke van Os-Medendorp, J E W C van Gemert-Pijnen, Rudolf M Verdaasdonk, Plinio Pelegrini Morita

Abstract read
In one paragraph

Article in JMIR nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. 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

7 authors.

Sjors GroeneveldResearch Group Technology, Health & Care, Saxion University of Applied Sciences, Enschede, Netherlands.ORCID 0000-0003-3942-9691
Gaya Bin NoonSchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.ORCID 0000-0002-1255-329X
Marjolein E M den OudenResearch Group Technology, Health & Care, Saxion University of Applied Sciences, Enschede, Netherlands.ORCID 0000-0001-7821-1578
Harmieke van Os-MedendorpDomain Health, Sports, and Welfare, Inholland University of Applied Sciences, Amsterdam, Netherlands.ORCID 0000-0003-3300-2101
J E W C van Gemert-PijnenCentre for eHealth and Wellbeing Research, Section of Psychology, Health and Technology, University of Twente, Enschede, Netherlands.ORCID 0000-0001-6511-7240
Rudolf M VerdaasdonkTechMed Center, Health Technology Implementation, University of Twente, Enschede, Netherlands.ORCID 0000-0002-3115-637X
Plinio Pelegrini MoritaSchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.ORCID 0000-0001-9515-6478

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult's home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague.

Indexed as

Artificial IntelligenceLife StyleLong-Term CareAgedFemaleHumansMonitoring, Physiologicagingaging in placealgorithmambient assisted livingartificial intelligenceassisted livingdatadigital healthgeriatricgeriatricsgerontologyhealth care professionalhealth care professionalshealth monitoringhealth professionalhealth professionalshealth promotionhealth technologyindependent livingmachine learningnursenursesolder adultssmart homesmart homes

Identifiers

PMID38781012
PMCPMC11157177

What OpenQuestion holds

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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.