Evidence map›Paper›PMID 33750340›Full record

ArticleBMC pregnancy and childbirth2021

Explaining the impact of mHealth on maternal and child health care in low- and middle-income countries: a realist synthesis.

Eveline M Kabongo, Ferdinand C Mukumbang, Peter Delobelle, Edward Nicol

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 7 pooled it
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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

50 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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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.

Eveline M KabongoDivision of Health Systems and Public Health, Stellenbosch University, Cape Town, South Africa. evekabo@gmail.com.ORCID http://orcid.org/0000-0002-6651-9097
Ferdinand C MukumbangBurden of Disease Research Unit, South African Medical Research Council, Cape Town, South Africa.
Peter DelobelleSchool of Public Health, University of the Western Cape, Cape Town, South Africa.
Edward NicolDivision of Health Systems and Public Health, Stellenbosch University, Cape Town, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite the growing global application of mobile health (mHealth) technology in maternal and child health, contextual factors, and mechanisms by which interventional outcomes are generated, have not been subjected to a systematic examination. In this study, we sought to uncover context, mechanisms, and outcome elements of various mHealth interventions based on implementation and evaluation studies to formulate theories or models explicating how mHealth interventions work (or not) both for health care providers and for pregnant women and mothers.

methodWe undertook a realist synthesis. An electronic search of five online databases (PubMed/Medline, Google Scholar, Scopus, Academic Search Premier and Health Systems Evidence) was performed. Using appropriate Boolean phrases terms and selection procedures, 32 articles were identified. A theory-driven approach, narrative synthesis, was applied to synthesize the data. Thematic content analysis was used to delineate elements of the intervention, including its context, actors, mechanisms, and outcomes. Abduction and retroduction were applied using a realist evaluation heuristic tool to formulate generative theories.

resultsWe formulated two configurational models illustrating how and why mHealth impacts implementation and uptake of maternal and child health care. Implementation-related mechanisms include buy-in from health care providers, perceived support of health care providers' motivation and perceived ease of use and usefulness. These mechanisms are influenced by adaptive health system conditions including organization, resource availability, policy implementation dynamics, experience with technology, network infrastructure and connectivity. For pregnant women and mothers, mechanisms that trigger mHealth use and consequently uptake of maternal and child health care include perceived satisfaction, motivation and positive psychological support. Information overload was identified as a potential negative mechanism impacting the uptake of maternal and child health care. These mechanisms are influenced by health system conditions, socio-cultural characteristics, socio-economic and demographics characteristics, network infrastructure and connectivity and awareness.

conclusionModels developed in this study provide a detailed understanding of implementation and uptake of mHealth interventions and how and why they impact maternal and child health care in low- and middle-income countries. These models provide a foundation for the 'white box' of theory-driven evaluation of mHealth interventions and can improve rollout and implementation where required.

Indexed as

Models, OrganizationalChildChild, PreschoolDeveloping CountriesFemaleHumansIncomeInfantInfant, NewbornMaleMaternal-Child Health ServicesMothersPerinatal CarePregnancyTelemedicineChild healthLow- and middle-income countriesMaternal healthmHealthMobile phoneRealist synthesis

Identifiers

PMID33750340
PMCPMC7941738

What OpenQuestion holds

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LicenceCC BY
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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.