Evidence map›Paper›PMID 34047703›Full record

ArticleJMIR formative research2021

A Digital Platform for Facilitating Personalized Dementia Care in Nursing Homes: Formative Evaluation Study.

Gubing Wang, Armagan Albayrak, Gerd Kortuem, Tischa Jm van der Cammen

Abstract read
In one paragraph

Article in JMIR formative research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Know-Me: A Toolkit for Designing Personalised Dementia Care.International journal of environmental research and public health · 2021
    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

4 authors.

Gubing WangFaculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.ORCID https://orcid.org/0000-0003-4847-534X
Armagan AlbayrakFaculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.ORCID https://orcid.org/0000-0001-5801-3670
Gerd KortuemFaculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.ORCID https://orcid.org/0000-0003-3500-0046
Tischa Jm van der CammenFaculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.ORCID https://orcid.org/0000-0002-0383-3767

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCare personalization is key to the well-being of people with dementia according to person-centered care. With the development of the internet of things, a large quantity of personal data can be collected securely and reliably, which has the potential to facilitate care personalization for people with dementia. Yet, there are limited assistive technologies developed for this purpose, and the user acceptance of assistive technologies is low in nursing homes. Therefore, through a data-enabled design approach, a digital platform was developed for helping the care team in a nursing home to personalize dementia care, specifically in the management of behavioral and psychological dementia symptoms.

objectiveThis study aimed to evaluate the digital platform in a real-life context with potential users from the following two aspects: (1) to explore if the digital platform could help with generating insights on the current state of each person with dementia and (2) to gather feedback on the digital platform from the care team.

methodsThe digital platform was deployed in the nursing home for 7 weeks and the data collected were visualized and presented to the care team via the digital platform. The visualizations were analyzed by the researchers for pattern detection. Meanwhile, the care team was asked to examine the visualizations and were interviewed for the following: (1) if any insights and actions were generated from the examination, (2) the usefulness of the digital platform, and (3) the improvements they would like to see.

resultsThe data collected on the digital platform demonstrated its potential for pattern detection. Insights were generated by the care team and categorized into "client level," "ward level," and "team level." The corresponding actions taken by the care team were classified into "investigation" and "implementation." User acceptance varied across the care team, and three aspects of improvement for the digital platform were identified.

conclusionsBy evaluating the digital platform, this study gained insights on applying data-enabled design for personalizing dementia care; besides, it offers future researchers some recommendations on how to integrate assistive technologies in the nursing home context.

Indexed as

assistive technologycare managementdata-driven designdata-enabled designdata visualizationshealth care designhuman-centered designinternet of thingspeople with dementiaperson-centered care

Identifiers

PMID34047703
PMCPMC8196358

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Registered trials

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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.