Evidence map›Paper›PMID 30305256›Full record

ArticleJMIR human factors2018

Developing Culturally Sensitive mHealth Apps for Caribbean Immigrant Women to Use During Pregnancy: Focus Group Study.

Hana AlJaberi

Open access · goldAbstract read
In one paragraph

Article in JMIR human factors, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

10 citing papers in PubMed, 21 citations in OpenAlex.

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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

1 author at 1 institution in 1 country.

Hana AlJaberiDepartment of Computer Graphics Technology, Purdue Polytechnic Institute, Purdue University, West Lafayette, IN, United States.ORCID http://orcid.org/0000-0003-0648-1581
Purdue University West Lafayette · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA valuable addition to the mobile health (mHealth) space is an exploration of the context of minorities in developed countries. The transition period postmigration, culture, and socioeconomic uniqueness of migratory groups can shed light on the problems with existing prenatal mHealth apps.

objectiveThe objectives of this study were to (1) use the theoretical concept of pregnancy ecology to understand the emotional, physical, information, and social challenges affecting low-income Caribbean immigrant women's prenatal well-being practices and (2) develop a deep understanding of challenges worthy of consideration in mHealth design for these women.

methodsThis qualitative interpretive approach using analytical induction presents the findings of 3 focus group sessions with 12 Caribbean immigrant women living in South Florida in the United States. The study took place from April to September 2015.

resultsThe participants revealed problematic tiers and support needs within the pregnancy ecology including emotional stressors caused by family separation, physical challenges, information gaps, and longing for social support.

conclusionsmHealth interventions for low-income Caribbean immigrant women must be designed beyond the conventional way of focusing on the events surrounding the unborn child. It can be tailored to the needs of the expecting mother. Pregnancy information should be customized on the basis of the variability of lifestyle, cultural practices, socioeconomic status, and social ties while still being able to deliver appropriate guidelines and clear cultural misconceptions.

Indexed as

Caribbeanhuman computer interactionimmigrant womenmHealthmobile phoneprenatal health

Identifiers

PMID30305256
PMCPMC6231776
OpenAlexW2888612494

What OpenQuestion holds

Textmetadata
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.