Evidence map›Paper›PMID 35684874›Full record

ReviewSensors (Basel, Switzerland)2022

Are Smart Homes Adequate for Older Adults with Dementia?

Gibson Chimamiwa, Alberto Giaretta, Marjan Alirezaie, Federico Pecora, Amy Loutfi

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2022. 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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  6. Review
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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

5 authors.

Gibson ChimamiwaCentre for Applied Autonomous Sensor Systems (AASS), Örebro University, 70281 Örebro, Sweden.
Alberto GiarettaCentre for Applied Autonomous Sensor Systems (AASS), Örebro University, 70281 Örebro, Sweden.ORCID 0000-0001-9293-7711
Marjan AlirezaieCentre for Applied Autonomous Sensor Systems (AASS), Örebro University, 70281 Örebro, Sweden.
Federico PecoraCentre for Applied Autonomous Sensor Systems (AASS), Örebro University, 70281 Örebro, Sweden.
Amy LoutfiCentre for Applied Autonomous Sensor Systems (AASS), Örebro University, 70281 Örebro, Sweden.

Funding

European Union Horizon 2020 and Marie Skłodowska-Curie 754285
6 · The paper itself

Abstract

Smart home technologies can enable older adults, including those with dementia, to live more independently in their homes for a longer time. Activity recognition, in combination with anomaly detection, has shown the potential to recognise users' daily activities and detect deviations. However, activity recognition and anomaly detection are not sufficient, as they lack the capacity to capture the progression of patients' habits across the different stages of dementia. To achieve this, smart homes should be enabled to recognise patients' habits and changes in habits, including the loss of some habits. In this study, we first present an overview of the stages that characterise dementia, alongside real-world personas that depict users' behaviours at each stage. Then, we survey the state of the art on activity recognition in smart homes for older adults with dementia, including the literature that combines activity recognition and anomaly detection. We categorise the literature based on goals, stages of dementia, and targeted users. Finally, we justify the necessity for habit recognition in smart homes for older adults with dementia, and we discuss the research challenges related to its implementation.

Indexed as

DementiaPalliative CareAgedHumansactivity recognitionageingdementiahabit recognitionsmart homes

Identifiers

PMID35684874
PMCPMC9185523

What OpenQuestion holds

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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.