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Record W4380576197 · doi:10.2196/45231

Implementing Active Assisted Living Technology in the Long-term Care of People Living With Dementia to Address Loneliness: European Survey

2023· article· en· W4380576197 on OpenAlexvenueno aff
Kübra Beliz Budak, Franziska Laporte Uribe, Franka Meiland, Simone Anna Felding, Sonja Teupen, Johannes Michael Bergmann, René Mueller‐Widmer, Martina Roes

Bibliographic record

VenueJMIR Aging · 2023
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsnot available
FundersHorizon 2020 Framework ProgrammeMarie Curie
KeywordsLonelinessDementiaContext (archaeology)Assisted livingLong-term careGerontologyPsychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: In the lives of people with dementia, loneliness is an important issue with psychological and physical consequences. Active assisted living (AAL) technology has been gaining visibility in the care of persons living with dementia, including addressing loneliness. However, to the best of our knowledge, there is a lack of evidence concerning the factors influencing the implementation of AAL technology within the context of dementia, loneliness, and long-term care (LTC). OBJECTIVE: We aimed to identify the familiarity with AAL technology that is promising for addressing loneliness in persons living with dementia in LTC in Europe and the factors influencing AAL technology implementation. METHODS: A web-based survey was developed based on findings from our previous literature review. The Consolidated Framework for Implementation Research guided the development and analysis of the survey. Participants included 24 representatives of Alzheimer Europe member associations from 15 European countries. The data were analyzed using basic statistical methods (descriptive statistics). RESULTS: The baby seal robot Paro was reported to be the most familiar AAL technology by 19 of 24 participants addressing loneliness in people with dementia living in LTC. Participants from Norway (n=2) reported familiarity with 14 AAL technologies, and participants from Serbia (n=1) reported zero familiarity. It seems that countries that invest less in LTC facilities are familiar with fewer AAL technologies. At the same time, these countries report a more positive attitude toward AAL technology, express a higher need for it, and see more advantages than disadvantages than those countries that invest more in LTC. However, a country's investment in LTC facilities does not seem to be linked to other implementation aspects such as costs, planning, and the impact of infrastructure. CONCLUSIONS: Implementation of AAL technology to address loneliness in dementia seems to be linked to familiarity with the technology in a country as well as national investment in LTC facilities. This survey confirms the literature on higher investment countries' critical stance in regard to AAL technology implementation to address loneliness in persons living with dementia living in LTC. Further research is needed to clarify the potential reasons why familiarity with more AAL technology does not seem to be directly linked with acceptance, positive attitude, or satisfaction with AAL technology addressing loneliness in persons living with dementia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.311
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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