Evidence map›Paper›PMID 40627502›Full record

ReviewJournal of applied gerontology : the official journal of the Southern Gerontological Society2026

Anomaly Detection Technologies for Dementia Care: Monitoring Goals, Sensor Applications, and Trade-offs in Home-Based Solutions-A Narrative Review.

Joy Lai, Bing Ye, Alex Mihailidis

Abstract readReview
In one paragraph

Review in Journal of applied gerontology : the official journal of the Southern Gerontological Society, 2026. 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. Review
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.

Joy LaiFaculty of Applied Science and Engineering, University of Toronto, Toronto, ON, Canada.ORCID 0009-0003-4841-486X
Bing YeKITE, Toronto Rehabilitation Institute, Toronto, ON, Canada.ORCID 0000-0002-8080-1224
Alex MihailidisFaculty of Applied Science and Engineering, University of Toronto, Toronto, ON, Canada.ORCID 0000-0003-2233-0919

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anomaly detection technologies are increasingly used to monitor people living with dementia (PLwD) in home settings, addressing critical behaviors such as wandering, sleep disturbances, and agitation. This narrative review examines technologies used for detecting behavioral anomalies, the activities they monitor, and the trade-offs between their benefits and limitations. A systematic search across MEDLINE, IEEE Xplore, ACM Digital Library, and Web of Science identified 78 studies, categorized through thematic analysis. Three primary motivations emerged: early diagnosis, safety monitoring, and reducing caregiver stress while promoting autonomy. Technologies include GPS tracking, wearables, environmental sensors, and smart home systems, each with benefits like real-time alerts and non-intrusive monitoring but also challenges such as user compliance, false positives, and privacy concerns. While these systems enhance safety and autonomy, improving sensor accuracy, integrating AI for personalized interventions, and addressing ethical concerns are essential for long-term effectiveness and supporting the well-being of both PLwD and caregivers.

Indexed as

DementiaHome Care ServicesCaregiversHumansMonitoring, PhysiologicWandering BehaviorWearable Electronic Devicesactivities of daily living (ADLs)anomaly detectionassisted livingassistive technologybehavior monitoringcaregivingdementia

Identifiers

PMID40627502
PMCPMC13076978

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.