Evidence map›Paper›PMID 32867716›Full record

ArticleBMC pregnancy and childbirth2020

RoadMApp: a feasibility study for a smart travel application to improve maternal health delivery in a low resource setting in Zimbabwe.

Zibusiso Nyati-Jokomo, Israel Mbekezeli Dabengwa, Liberty Makacha, Newton Nyapwere, Yolisa Prudence Dube, Laurine Chikoko, Marianne Vidler, Prestige Tatenda Makanga

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. The FemTech revolution: Unlocking the potential of new technology for optimizing pregnancy outcomes in low- and middle-income countries and remote areas.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
    Article
  2. Article
  3. Article
  4. Exploration of Telemidwifery: An Initiation of Application Menu in Indonesia.International journal of environmental research and public health · 2022
    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

8 authors.

Zibusiso Nyati-JokomoThe University of Zimbabwe, College of Health Sciences, Parirenyatwa Hospital, Harare, Zimbabwe. jokomob@gmail.com.
Israel Mbekezeli DabengwaNational University of Science and Technology, Faculty of Medicine, Mpilo Hospital, Bulawayo, Zimbabwe.
Liberty MakachaSurveying and Geomatics, Midlands State University, Senga, Gweru, Zimbabwe.
Newton NyapwereSurveying and Geomatics, Midlands State University, Senga, Gweru, Zimbabwe.
Yolisa Prudence DubeSurveying and Geomatics, Midlands State University, Senga, Gweru, Zimbabwe.
Laurine ChikokoResearch and Postgraduate Studies, Midlands State University, Gweru, Zimbabwe.
Marianne VidlerDepartment of Obstetrics and Gynaecology, The University of British Columbia, Vancouver, Canada.
Prestige Tatenda MakangaSurveying and Geomatics, Midlands State University, Senga, Gweru, Zimbabwe.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTravel time and healthcare financing are critical determinants of the provision of quality maternal health care in low resource settings. Despite the availability of pregnancy-related mHealth and smart travel applications, there is a lack of evidence on their usage to travel to health facilities for routine antenatal care and emergencies. There is a shortage of information about the feasibility of using a custom-made mobile technology that integrates smart travel and mHealth. This paper explores the feasibility of implementing a custom-made geographically enabled mobile technology-based tool (RoadMApp) to counter the adverse effects of long travel times for maternal care in Kwekwe District, Zimbabwe.

methodsWe frame the paper using the first two steps (listen & plan) of the Spiral Technology Action Research (STAR model). The paper uses an exploratory case study design and Participatory Learning Approaches (PLA) with stakeholders (community members) and in-depth interviews with key informants (health care service providers, pregnant women, transport operators). One hundred ninety-three participants took part in the study. We conducted focus group discussions with pregnant women, women of childbearing age, men (household heads), and elderly women. The discussion questions centered on travel time, availability of transport, cellular network coverage, and perceptions of the RoadMApp application. Data were analysed thematically using Nvivo Pro 12.

resultsMost parts of rural Kwekwe are far from health facilities and have an inefficient road and telecommunications network. Hence, it is hard to predict if RoadMApp will integrate into the lives of the community - especially those in rural areas. Since these issues are pillars of the design of the RoadMApp mHealth, the implementation will probably be a challenge.

conclusionCommunities are keen to embrace the RoadMApp application. However, the feasibility of implementing RoadMApp in Kwekwe District will be a challenge because of maternal health care barriers such as poor road network, poor phone network, and the high cost of transport. There is a need to investigate the social determinants of access to maternity services to inform RoadMApp implementation.

Indexed as

Health Services AccessibilityMaternal Health ServicesMobile ApplicationsPrenatal CareAdolescentAdultAgedFeasibility StudiesFemaleHumansMaleMiddle AgedTravelYoung AdultZimbabweBarriers to maternal health servicesGeographically enabled mHealthKwekweMobile healthPregnancy, transportRoadMApp

Identifiers

PMID32867716
PMCPMC7457488

What OpenQuestion holds

Textmetadata
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.