Evidence map›Paper›PMID 35162893›Full record

ArticleInternational journal of environmental research and public health2022

Addressing the Social Determinants of Health in South Korea: Moderating Role of mHealth Technologies.

Woohyun Yoo

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

1 author.

Woohyun YooDepartment of Mass Communication & Institute of Social Sciences, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea.ORCID 0000-0002-1434-8097

Funding

Incheon National University 2021
6 · The paper itself

Abstract

Mobile health (mHealth) technologies may reduce or widen health inequalities. Despite the extensive literature in support of both of these contrasting views, little attention has been paid to the role of mHealth technologies with regard to social strata and health in the context of South Korea, a country with one of the highest usages of smartphones worldwide. This study examined the effects of social determinants on health self-efficacy and health status and explored how mHealth technologies moderate the impacts of social determinants on health outcomes. Data were collected via online surveys from 29 July to 3 August 2021. Survey data from 1187 Korean adults showed that men had higher levels of health self-efficacy than women. The higher an individual's education level, the greater their subjective health status. Individuals with higher levels of monthly household income, social capital, and healthcare quality reported higher levels of health self-efficacy and superior health status. The use of mHealth technologies moderated the associations between social determinants and health outcomes. Specifically, monthly household income and social capital had smaller effects on health self-efficacy and health status among those who used higher levels of mHealth technologies. Among higher users of mHealth technologies, females reported better health status than males, while men showed better health status than women in the low-user group. These findings highlight the effectiveness of mHealth technologies in addressing health disparities.

Indexed as

Social Determinants of HealthTelemedicineAdultBiomedical TechnologyFemaleHumansMaleRepublic of KoreaTechnologyhealth inequalityhealth self-efficacyhealth statusmHealthmobile health technologysocial determinants of health

Identifiers

PMID35162893
PMCPMC8834917

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.