Evidence map›Paper›PMID 31906521›Full record

ArticleJournal of clinical medicine2020

Relationship between Morbidity and Health Behavior in Chronic Diseases.

Munjae Lee, Sewon Park, Kyu-Sung Lee

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
6.2field-weighted citation impact, top 3% 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

25 citing papers in PubMed, 45 citations in OpenAlex.

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  17. Digital Health Care Industry Ecosystem: Network Analysis.Journal of medical Internet research · 2022
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  20. A population-based study of health-promoting behaviors and their predictors in Iranian males, 2019.Archives of public health = Archives belges de sante publique · 2021
    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

3 authors at 1 institution in 1 country.

Munjae LeeResearch Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Korea.
Sewon ParkResearch Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Korea.
Kyu-Sung LeeDepartment of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul 06355, Korea.
Samsung Medical Center · KR

Funding

National Research Foundation of Korea NRF-2019S1A5A2A03040304
6 · The paper itself

Abstract

This study aimed to analyze the demographic characteristics and health behaviors related to chronic diseases and to identify factors that may affect chronic diseases. Data from the Seventh Korea National Health and Nutrition Examination Survey were used, and 3795 adults aged above 40 years were included. The following demographic variables were obtained: sex, age, education, income, type of health insurance, and private insurance. The following health behavior factors were also analyzed: medical checkup, drinking, smoking, exercise, obesity, and hypercholesterolemia. Participants with lower socioeconomic status had a higher risk of developing chronic diseases. Meanwhile, those with private health insurance had a lower risk of developing chronic diseases. In addition, participants who underwent medical checkups and performed exercises had a lower risk, while those with obesity and hypercholesterolemia had a higher risk of developing chronic diseases. It is necessary to manage chronic diseases through comprehensive programs, rather than managing these diseases individually, and through community primary care institutions to improve health behaviors.

Indexed as

chronic diseasehealth behaviorKoreaprimary caresocioeconomic status

Identifiers

PMID31906521
PMCPMC7020052
OpenAlexW2997502163

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.