Evidence map›Paper›PMID 38169136›Full record

ArticleGeriatrics & gerontology international2024

What drives low-income older adults' intention to use mobility applications?

Diana Yian Lian Chan, Chun Yong Chong, Pei-Lee Teh, Shaun Wen Huey Lee

Abstract read
In one paragraph

Article in Geriatrics & gerontology international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
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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

4 authors.

Diana Yian Lian ChanSchool of Business, Monash University Malaysia, Bandar Sunway, Malaysia.
Chun Yong ChongSchool of Information Technology, Monash University Malaysia, Bandar Sunway, Malaysia.
Pei-Lee TehSchool of Business, Monash University Malaysia, Bandar Sunway, Malaysia.
Shaun Wen Huey LeeGerontechnology Laboratory, Monash University Malaysia, Bandar Sunway, Malaysia.ORCID https://orcid.org/0000-0001-7361-6576

Funding

Ministry of Higher Education, Malaysia GOV-000047Ministry of Higher Education, Malaysia LR005-2019Ministry of Higher Education, Malaysia LRGS/1/2019/UM/01/1/1
6 · The paper itself

Abstract

aimMobility applications have the potential to support low-income older adults in facing mobility challenges. However, there is a generally lower uptake of technology in this segment. To understand factors affecting the intention to use a mobility app, we drew upon the Protection Motivation Theory, and tested a model of low-income older adults' technology adoption.

methodsA cross-sectional survey was conducted across seven states in Malaysia among community-dwelling low-income older adults aged ≥60 years old (n = 282). Measurement items were adapted from pre-validated scales and 7-point Likert Scales were used. Partial least squares structural equation modeling was utilized to assess the hypothesized model.

resultsMobility technology awareness was found to shape an individual's threat and coping appraisals associated with their intention to use a mobility app. The decision of a low-income older adult to adopt a mobility app as a protective action is not a direct function of threat and coping appraisals but is indirect, and mediated by the underlying cost-benefit perceptions of non-adoption and adoption of the mobility app. In terms of technology perceptions, perceived usefulness is a significant predictor, but not perceived ease of use.

conclusionsThis study entails a new model by uncovering the psychological factors encompassing mobility technology awareness, threat-coping appraisals, and cost-benefit perceptions on Technology Acceptance Model studies. These insights have important implications for the development and implementation of a mobility app among low-income older adults. Geriatr Gerontol Int 2024; 24: 342-350.

Indexed as

IntentionMobile ApplicationsAgedCoping SkillsCross-Sectional StudiesHumansMotivationlow‐incomemobility appolder adultsprotection motivation theorytechnology adoption

Identifiers

PMID38169136
PMCPMC11503575

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LicenceCC BY
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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.