Evidence map›Paper›PMID 32134390›Full record

ArticleJournal of medical Internet research2020

Impact of Illness on Electronic Health Use (The Seventh Tromsø Study - Part 2): Population-Based Questionnaire Study.

Luis Marco-Ruiz, Rolf Wynn, Sunday Oluwafemi Oyeyemi, Andrius Budrionis, Kassaye Yitbarek Yigzaw, Johan Gustav Bellika

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.5field-weighted citation impact, top 13% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Article
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  4. Special Issue on E-Health Services.International journal of environmental research and public health · 2020
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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

6 authors at 2 institutions in 1 country.

Luis Marco-RuizNorwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway.ORCID 0000-0001-6349-3162
Rolf WynnDepartment of Clinical Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.ORCID 0000-0002-2254-3343
Sunday Oluwafemi OyeyemiDepartment of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.ORCID 0000-0002-5689-5152
Andrius BudrionisNorwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway.ORCID 0000-0002-9525-1923
Kassaye Yitbarek YigzawNorwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway.ORCID 0000-0001-7068-117X
Johan Gustav BellikaNorwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway.ORCID 0000-0003-2893-6989
University Hospital of North Norway · NOUiT The Arctic University of Norway · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients who suffer from different diseases may use different electronic health (eHealth) resources. Thus, those who plan eHealth interventions should take into account which eHealth resources are used most frequently by patients that suffer from different diseases.

objectiveThe aim of this study was to understand the associations between different groups of chronic diseases and the use of different eHealth resources.

methodsData from the seventh survey of the Tromsø Study (Tromsø 7) were analyzed to determine how different diseases influence the use of different eHealth resources. Specifically, the eHealth resources considered were use of apps, search engines, video services, and social media. The analysis contained data from 21,083 participants in the age group older than 40 years. A total of 15,585 (15,585/21,083; 73.92%) participants reported to have suffered some disease, 10,604 (10,604/21,083; 50.29%) participants reported to have used some kind of eHealth resource in the last year, and 7854 (7854/21,083; 37.25%) participants reported to have used some kind of eHealth resource in the last year and suffered (or had suffered) from some kind of specified disease. Logistic regression was used to determine which diseases significantly predicted the use of each eHealth resource.

resultsThe use of apps was increased among those individuals that (had) suffered from psychological problems (odds ratio [OR] 1.39, 95% CI 1.23-1.56) and cardiovascular diseases (OR 1.12, 95% CI 1.01-1.24) and those part-time workers that (had) suffered from any of the diseases classified as others (OR 2.08, 95% CI 1.35-3.32). The use of search engines for accessing health information increased among individuals who suffered from psychological problems (OR 1.39, 95% CI 1.25-1.55), cancer (OR 1.26, 95% CI 1.11-1.44), or any of the diseases classified as other diseases (OR 1.27, 95% CI 1.13-1.42). Regarding video services, their use for accessing health information was more likely when the participant was a man (OR 1.31, 95% CI 1.13-1.53), (had) suffered from psychological problems (OR 1.70, 95% CI 1.43-2.01), or (had) suffered from other diseases (OR 1.43, 95% CI 1.20-1.71). The factors associated with an increase in the use of social media for accessing health information were as follows: (had) suffered from psychological problems (OR 1.65, 95% CI 1.42-1.91), working part time (OR 1.35, 95% CI 0.62-2.63), receiving disability benefits (OR 1.42, 95% CI 1.14-1.76), having received an upper secondary school education (OR 1.20, 95% CI 1.03-1.38), being a man with a high household income (OR 1.67, 95% CI 1.07-2.60), suffering from cardiovascular diseases and having a high household income (OR 3.39, 95% CI 1.62-8.16), and suffering from respiratory diseases while being retired (OR 1.95, 95% CI 1.28-2.97).

conclusionsOur findings show that different diseases are currently associated with the use of different eHealth resources. This knowledge is useful for those who plan eHealth interventions as they can take into account which type of eHealth resource may be used for gaining the attention of the different user groups.

Indexed as

AdultChronic DiseaseCross-Sectional StudiesFemaleHumansMaleMiddle AgedResearch DesignSurveys and QuestionnairesTelemedicineeHealthinternetmobile appssearch enginessocial media

Identifiers

PMID32134390
PMCPMC7082738
OpenAlexW2976802096

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.