Evidence map›Paper›PMID 29066425›Full record

ArticleJMIR mHealth and uHealth2017

Mobile Technology Use Across Age Groups in Patients Eligible for Cardiac Rehabilitation: Survey Study.

Robyn Gallagher, Kellie Roach, Leonie Sadler, Helen Glinatsis, Julie Belshaw, Ann Kirkness, Ling Zhang, Patrick Gallagher, Glenn Paull, Yan Gao and 3 more

Open access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 2 pooled it
4.8field-weighted citation impact, top 4% of its field
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

46 citing papers in PubMed, 2 syntheses or guidelines pooled it, 87 citations in OpenAlex.

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  16. Online Health Information-Seeking Behaviours and eHealth Literacy among First-Generation Chinese Immigrants.International journal of environmental research and public health · 2023
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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

13 authors at 7 institutions in 2 countries.

Robyn GallagherCharles Perkins Centre, Sydney Nursing School, University of Sydney, Camperdown, Australia.ORCID http://orcid.org/0000-0001-5588-9351
Kellie RoachRyde Hospital, Northern Sydney Local Health District, Sydney, Australia.ORCID http://orcid.org/0000-0003-0823-0751
Leonie SadlerManly Hospital, Northern Sydney Local Health District, Sydney, Australia.ORCID http://orcid.org/0000-0002-6041-5094
Helen GlinatsisRoyal North Shore Hospital, Northern Sydney Local Health District, Sydney, Australia.ORCID http://orcid.org/0000-0002-8184-5587
Julie BelshawHornsby Ku-ring-gai Hospital, Northern Sydney Local Health District, Sydney, Australia.ORCID http://orcid.org/0000-0001-8342-1270
Ann KirknessRoyal North Shore Hospital, Northern Sydney Local Health District, Sydney, Australia.ORCID http://orcid.org/0000-0002-0243-0421
Ling ZhangCharles Perkins Centre, Sydney Nursing School, University of Sydney, Camperdown, Australia.ORCID http://orcid.org/0000-0002-9961-2079
Patrick GallagherCharles Perkins Centre, Sydney Nursing School, University of Sydney, Camperdown, Australia.ORCID http://orcid.org/0000-0002-9793-1746
Glenn PaullSt George Hospital, South Eastern Sydney Local Health District, Sydney, Australia.ORCID http://orcid.org/0000-0001-6842-342X
Yan GaoSt George Hospital, South Eastern Sydney Local Health District, Sydney, Australia.ORCID http://orcid.org/0000-0002-0498-5682
Stephanie Ruth PartridgeCharles Perkins Centre, Sydney School of Public Health, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0001-5390-3922
Helen ParkerCharles Perkins Centre, Faculty of Health Sciences, University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0002-3826-2765
Lis NeubeckSchool of Health and Social Care, Edinburgh Napier University, Edinburgh, United Kingdom.ORCID http://orcid.org/0000-0001-5852-1034
The University of Sydney · AURoyal North Shore Hospital · AUSt George Hospital · AUEdinburgh Napier University · GBHornsby Ku-ring-gai Hospital · AUManly Hospital · AUNorthern Sydney Local Health District · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEmerging evidence indicates mobile technology-based strategies may improve access to secondary prevention and reduce risk factors in cardiac patients. However, little is known about cardiac patients' use of mobile technology, particularly for health reasons and whether the usage varies across patient demographics.

objectiveThis study aimed to describe cardiac patients' use of mobile technology and to determine variations between age groups after adjusting for education, employment, and confidence with using mobile technology.

methodsCardiac patients eligible for attending cardiac rehabilitation were recruited from 9 hospital and community sites across metropolitan and rural settings in New South Wales, Australia. Participants completed a survey on the use of mobile technology devices, features used, confidence with using mobile technology, willingness and interest in learning, and health-related use.

resultsThe sample (N=282) had a mean age of 66.5 (standard deviation [SD] 10.6) years, 71.9% (203/282) were male, and 79.0% (223/282) lived in a metropolitan area. The most common diagnoses were percutaneous coronary intervention (33.3%, 94/282) and myocardial infarction (22.7%, 64/282). The majority (91.1%, 257/282) used at least one type of technology device, 70.9% (200/282) used mobile technology (mobile phone/tablet), and 31.9% (90/282) used all types. Technology was used by 54.6% (154/282) for health purposes, most often to access information on health conditions (41.4%, 117/282) and medications (34.8%, 98/282). Age had an important independent association with the use of mobile technology after adjusting for education, employment, and confidence. The youngest group (<56 years) was over 4 times more likely to use any mobile technology than the oldest (>69 years) age group (odds ratio [OR] 4.45, 95% CI 1.46-13.55), 5 times more likely to use mobile apps (OR 5.00, 95% CI 2.01-12.44), and 3 times more likely to use technology for health-related reasons (OR 3.31, 95% CI 1.34-8.18). Compared with the older group, the middle age group (56-69 years) was more than twice as likely to use any mobile technology (OR 2.42, 95% CI 1.27-4.59) and mobile technology for health-related purposes (OR 1.92, 95% CI 1.04-3.53). Participants who had completed high school were twice as likely to use mobile technology (OR 2.62, 95% CI 1.45-4.70), mobile apps (OR 2.05, 95% CI 1.09-3.84), and mobile technology for health-related reasons (OR 5.09, 95% CI 2.89-8.95) than those who had not completed high school. Associations were also present between participants living in metropolitan areas and mobile technology use (OR 1.07, 95% CI 1.07-4.24) and employment and mobile app use (OR 2.72, 95% CI 1.44-5.140).

conclusionsMobile technology offers an important opportunity to improve access to secondary prevention for cardiac patients, particularly when modified to suit subgroups. High levels of mobile technology use and health motivation need to be harnessed for secondary prevention.

Indexed as

cardiac rehabilitationcardiovascular diseasecell phonedigital dividemobile phone

Identifiers

PMID29066425
PMCPMC5676027
OpenAlexW2766397352

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.