Evidence map›Paper›PMID 35368316›Full record

ArticlePersonal and ubiquitous computing2022

Modelling mobile-based technology adoption among people with dementia.

Priyanka Chaurasia, Sally McClean, Chris D Nugent, Ian Cleland, Shuai Zhang, Mark P Donnelly, Bryan W Scotney, Chelsea Sanders, Ken Smith, Maria C Norton and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Personal and ubiquitous computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

11 authors.

Priyanka ChaurasiaSchool of Computing and Intelligent Systems, Ulster University, Londonderry, UK.ORCID 0000-0003-4249-3678
Sally McCleanSchool of Computing, Ulster University, Londonderry, UK.
Chris D NugentSchool of Computing, Ulster University, Londonderry, UK.
Ian ClelandSchool of Computing, Ulster University, Londonderry, UK.
Shuai ZhangSchool of Computing, Ulster University, Londonderry, UK.
Mark P DonnellySchool of Computing, Ulster University, Londonderry, UK.
Bryan W ScotneySchool of Computing, Ulster University, Londonderry, UK.
Chelsea SandersDepartment of Psychology, Utah State University, Logan, USA.
Ken SmithDepartment of Family and Consumer Studies, University of Utah, Salt Lake City, USA.
Maria C NortonDepartment of Family, Consumer, and Human Development, Utah State University, Logan, USA.
JoAnn TschanzDepartment of Psychology, Utah State University, Logan, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The work described in this paper builds upon our previous research on adoption modelling and aims to identify the best subset of features that could offer a better understanding of technology adoption. The current work is based on the analysis and fusion of two datasets that provide detailed information on background, psychosocial, and medical history of the subjects. In the process of modelling adoption, feature selection is carried out followed by empirical analysis to identify the best classification models. With a more detailed set of features including psychosocial and medical history information, the developed adoption model, using

Indexed as

Assistive technologiesDementiaMedical historyReminder applicationTechnology adoption

Identifiers

PMID35368316
PMCPMC8933362

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.