Evidence map›Paper›PMID 41042929›Full record

ArticleThe Gerontologist2025

Factors that influence the implementation of AI-driven lifestyle monitoring in long-term care for older adults.

Sjors Groeneveld, Tessa Dekkers, Lisette van Gemert-Pijnen, Ruud Verdaasdonk, Tijne Verveda, Robin Witteveen, Harmieke van Os-Medendorp, Marjolein den Ouden

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In one paragraph

Article in The Gerontologist, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Sjors GroeneveldResearch Group Technology, Health & Care, School of Social Work, Saxion University of Applied Sciences, Enschede, The Netherlands.ORCID 0000-0003-3942-9691
Tessa DekkersCentre for eHealth and Wellbeing Research, Health and Technology, University of Twente, Enschede, The Netherlands.ORCID 0000-0003-2787-3293
Lisette van Gemert-PijnenCentre for eHealth and Wellbeing Research, Health and Technology, University of Twente, Enschede, The Netherlands.ORCID 0000-0001-6511-7240
Ruud VerdaasdonkTechMed Center, Health Technology Implementation, University of Twente, Enschede, The Netherlands.ORCID 0000-0002-3115-637X
Tijne VervedaCentre for eHealth and Wellbeing Research, Health and Technology, University of Twente, Enschede, The Netherlands.
Robin WitteveenCentre for eHealth and Wellbeing Research, Health and Technology, University of Twente, Enschede, The Netherlands.
Harmieke van Os-MedendorpFaculty Health, Sports, and Social Work, Inholland University of Applied Sciences, Amsterdam, The Netherlands.ORCID 0000-0003-3300-2101
Marjolein den OudenResearch Group Technology, Health & Care, School of Social Work, Saxion University of Applied Sciences, Enschede, The Netherlands.ORCID 0000-0001-7821-1578

Funding

NWO
6 · The paper itself

Abstract

BACKGROUND AND

objectivesAI-driven lifestyle monitoring systems collect data from ambient, motion, contact, light, and physiological sensors placed in the home, enabling AI algorithms to identify daily routines and detect deviations to support older adults "aging in place." Despite its potential to support several challenges in long-term care for older adults, implementation remains limited. This study explored the facilitators and barriers to implementing AI-driven lifestyle monitoring in long-term care for older adults, as perceived by formal and informal caregivers, as well as management, in both an adopting and nonadopting healthcare organization. RESEARCH DESIGN AND

methodsA qualitative interview study using semi-structured interviews was conducted with 22 participants (5 informal caregivers, 10 formal caregivers, and 7 participants in a management position) from two long-term care organizations. Reflexive thematic analysis, guided by the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework, structured findings into facilitators and barriers.

resultsIn all, 12 facilitators and 16 barriers were identified, highlighting AI-driven lifestyle monitoring as a valuable, patient-centered, and unobtrusive tool enhancing care efficiency and caregiver reassurance. However, barriers such as privacy concerns, notification overload, training needs, and organizational alignment must be addressed. Contextual factors, including regulations, partnerships, and financial considerations, further influence implementation. DISCUSSION AND IMPLICATIONS: This study showed that to optimize implementation of AI-driven lifestyle monitoring, organizations should address privacy concerns, provide training, engage in system (re)design, and create a shared vision. A comprehensive multi-level approach across all levels is essential for successful AI integration in long-term care for older adults.

Indexed as

Artificial IntelligenceLife StyleLong-Term CareAgedAged, 80 and overCaregiversFemaleHumansInterviews as TopicMaleMiddle AgedQualitative ResearchAgeing in placeArtificial intelligenceImplementationMonitoring

Identifiers

PMID41042929
PMCPMC12688438

What OpenQuestion 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 OpenQuestion graph.