Evidence map›Paper›PMID 39269755›Full record

ArticleJMIR public health and surveillance2024

Digital Health Technology Use Across Socioeconomic Groups Prior to and During the COVID-19 Pandemic: Panel Study.

Inge Tuitert, Jesse D Marinus, Jelle R Dalenberg, Job Tb van 't Veer

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  15. A Method for Enabling Digital Health Technologies in Clinical and Translational Research at an Academic Medical Center.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025
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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.

Inge TuitertAcademy of Health & Social Studies, NHL Stenden University of Applied Sciences, Leeuwarden, Netherlands.ORCID 0000-0001-6338-3131
Jesse D MarinusPlanbureau Fryslân, Leeuwarden, Netherlands.ORCID 0000-0002-5781-9741
Jelle R DalenbergDepartment of Neurology, University Medical Center Groningen, Groningen, Netherlands.ORCID 0000-0001-8580-5358
Job Tb van 't VeerAcademy of Health & Social Studies, NHL Stenden University of Applied Sciences, Leeuwarden, Netherlands.ORCID 0000-0002-1872-9209

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigital technologies have become more important in the health care sector in the past decades. This transition from conventional to digital health care has been accelerated by the impact of the COVID-19 pandemic, which poses the risk of creating a "digital divide," inadvertently placing those who are older, economically disadvantaged, and have a lower level of education at a disadvantage.

objectiveThis study focuses on the influence of socioeconomic factors on the adoption of digital health technology in the Frisian population and how this relation is affected by the COVID-19 pandemic.

methodsIn 2019 and 2020, a panel study was conducted on digital health in the Frisian population in the Netherlands. In the survey, the use of digital health technology was operationalized in a broad sense, going beyond the care context by also including preventative health-promoting solutions generally available on the consumer market, such as wearables and lifestyle apps. First, to assess the influence of socioeconomic factors on the total use of digital health apps, a generalized linear model was fitted with use of digital health app as the dependent variable and socioeconomic factors as between-subject factors on the 2019 data. Second, to analyze whether the use of separate health apps increased from 2019 to 2020, we conducted chi-square tests on different digital health app types. Third, to examine the influence of COVID-19 on the use of digital health apps, a generalized linear mixed model was fitted with the use of digital health apps as the dependent variable, COVID-19 as the within-subject variable, and socioeconomic factors as between-subject factors.

resultsThe results indicated that prior to the COVID-19 pandemic, digital health technology use was higher in women, younger people, and those who are well educated and economically more privileged. Moreover, the percentage of people who reported using digital health technology rose from 70% (1580/2258) to 82.5% (1812/2197) due to the COVID-19 pandemic. This increase was significant for all separate types of digital health technology (all P<.001). In addition, we found the interaction effects of COVID-19 with age and education attainment, indicating that the lower total use among older people and people with lower education attainment became slightly less apparent from 2019 to 2020.

conclusionsThese findings on the influence of the COVID-19 pandemic on the digital divide indicated that the use of all types of digital health apps increased and that older individuals and people with a lower level of education caught up a little during COVID-19. Future research should gain more insight into this effect and examine whether it persists beyond the COVID-19 pandemic. Additionally, future endeavors should focus on vulnerable groups, ensuring they receive adequate attention to guarantee access to health care, preventative health-promoting solutions, and social services.

Indexed as

COVID-19Digital TechnologySocioeconomic FactorsAdolescentAdultAgedDigital HealthFemaleHumansMaleMiddle AgedMobile ApplicationsNetherlandsPandemicsSurveys and QuestionnairesTelemedicineadoptiondigital dividedigital healthdigital health appshealth technologylongitudinalsocioeconomicssurveystechnology usevulnerable groups

Identifiers

PMID39269755
PMCPMC11437226

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.