Evidence map›Paper›PMID 28982646›Full record

ArticleJMIR public health and surveillance2017

The Association Between Commonly Investigated User Factors and Various Types of eHealth Use for Self-Care of Type 2 Diabetes: Case of First-Generation Immigrants From Pakistan in the Oslo Area, Norway.

Naoe Tatara, Hugo Lewi Hammer, Hege Kristin Andreassen, Jelena Mirkovic, Marte Karoline Råberg Kjøllesdal

Erratum issuedAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Naoe Tatara *Department of Computer Science, Faculty of Technology, Art and Design, Oslo and Akershus University College of Applied Sciences, Oslo, Norway.ORCID http://orcid.org/0000-0002-0049-1634
Hugo Lewi HammerDepartment of Computer Science, Faculty of Technology, Art and Design, Oslo and Akershus University College of Applied Sciences, Oslo, Norway.ORCID http://orcid.org/0000-0001-9429-7148
Hege Kristin AndreassenCentre for Care Research, Norwegian University of Science and Technology, Gjøvik, Norway.ORCID http://orcid.org/0000-0001-6254-6023
Jelena MirkovicCenter for Shared Decision Making and Collaborative Care Research, Oslo University Hospital, Oslo, Norway.ORCID http://orcid.org/0000-0001-6828-1230
Marte Karoline Råberg KjøllesdalDepartment of Community Medicine and Global Health, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway.ORCID http://orcid.org/0000-0002-5223-711X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSociodemographic and health-related factors are often investigated for their association with the active use of electronic health (eHealth). The importance of such factors has been found to vary, depending on the purpose or means of eHealth and the target user groups. Pakistanis are one of the biggest immigrant groups in the Oslo area, Norway. Due to an especially high risk of developing type 2 diabetes (T2D) among this population, knowledge about their use of eHealth for T2D self-management and prevention (self-care) will be valuable for both understanding this vulnerable group and for developing effective eHealth services.

objectiveThe aim of this study was to examine how commonly were the nine types of eHealth for T2D self-care being used among our target group, the first-generation Pakistani immigrants living in the Oslo area. The nine types of eHealth use are divided into three broad categories based on their purpose: information seeking, communication, and active self-care. We also aimed to investigate how sociodemographic factors, as well as self-assessment of health status and digital skills are associated with the use of eHealth in this group.

methodsA survey was carried out in the form of individual structured interviews from September 2015 to January 2016 (N=176). For this study, dichotomous data about whether or not an informant had used each of the nine types of eHealth in the last 12 months and the total number of positive answers were used as dependent variables in a regression analysis. The independent variables were age, gender, total years of education, digital skills (represented by frequency of asking for help when using information and communication technology [ICT]), and self-assessment of health status. Principal component analyses were applied to make categories of independent variables to avoid multicollinearity.

resultsPrincipal component analysis yielded three components: knowledge, comprising total years of education and digital skills; health, comprising age and self-assessment of health status; and gender, as being a female. With the exception of closed conversation with a few specific acquaintances about self-care of T2D (negatively associated, P=.02) and the use of ICT for relevant information-seeking by using search engines (not associated, P=.18), the knowledge component was positively associated with all the other dependent variables. The health component was negatively associated with the use of ICT for closed conversation with a few specific acquaintances about self-care of T2D (P=.01) but not associated with the other dependent variables. Gender component showed no association with any of the dependent variables.

conclusionsIn our sample, knowledge, as a composite measure of education and digital skills, was found to be the main factor associated with eHealth use regarding T2D self-care. Enhancing digital skills would encourage and support more active use of eHealth for T2D self-care.

Indexed as

immigrantsinformation seeking behaviorself-caretype 2 diabetes

Identifiers

PMID28982646
PMCPMC5649041

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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.