Evidence map›Paper›PMID 37559100›Full record

ArticleBMC medical informatics and decision making2023

The protocol of DECO-MOM: a clinical trial testing the effectiveness of a mobile application for an environmental health intervention among pregnant women.

Hyun Kyoung Kim

Abstract readClinical Trial Protocol
In one paragraph

Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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. Article
  2. Article
  3. Review
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.

Hyun Kyoung KimDepartment of Nursing, Kongju National University, Gongju, South Korea. hkk@kongju.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEnvironmental toxins are particularly harmful to pregnant women and their fetuses due to the long-term effects of these toxins on children after birth. Environmental health behaviors can prevent and protect mothers and their babies' environmental health. METHODS/

designThis study presents a protocol for a double-blinded randomized controlled trial to examine the effect of a mobile application named "DECO-MOM" (Deep ECOlogy-MOM) for pregnant women. This application contains content related to environmental perceptions and behaviors according to the revised protection motivation theory. The mobile intervention will be implemented for 4 weeks for 40 pregnant women with a gestational age in the first trimester. As the control group, 40 pregnant women will be recruited at public healthcare centers in Chuncheon and Gongju in South Korea. The hypothesis is that the experimental group will have higher scores for environmental perceptions and behaviors than the control group. An online survey administered before and after the intervention will contain outcome parameters including depression, stress, quality of life, e-learning satisfaction, environmental perceptions, and environmental behaviors. DISCUSSION: This study will elaborate a guide for an intervention to enhance the effects of the mobile application on strengthening environmental health perceptions, behaviors, e-learning satisfaction, and quality of life and curtailing depression and stress among pregnant women.

trial registrationTrial registration Number: KCT0007725, Registered September 22, 2022. Prospectively registered.

Indexed as

Mobile ApplicationsPregnant PeopleChildEnvironmental HealthFemaleHumansInfantMothersPregnancyQuality of LifeRandomized Controlled Trials as TopicEnvironmental healthHealth behaviorsMobile applicationPregnancyWomen

Identifiers

PMID37559100
PMCPMC10410822

What OpenQuestion holds

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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.