mHealth clinical decision-making tools for maternal and perinatal health care in Sub-Saharan Africa: A systematic review protocol

Gaudensia A. Olomi; Lottie G. Cansdale; Simon Woodworth; Rachel Manongi; Pendo Mlay; Karen Yeates; Nicola West; Jane E. Hirst; Christopher Redman; Michael J. Mahande; Blandina T. Mmbaga; Ali S. Khashan

doi:10.12688/hrbopenres.13799.1

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Study Protocol

mHealth clinical decision-making tools for maternal and perinatal health care in Sub-Saharan Africa: A systematic review protocol

[version 1; peer review: 1 approved with reservations]

Gaudensia A. Olomi ^1-3, Lottie G. Cansdale⁴, Simon Woodworth^5,6, [...] Rachel Manongi¹, Pendo Mlay^1,7, Karen Yeates⁸, Nicola West⁸, Jane E. Hirst^9,10, Christopher Redman¹⁰, Michael J. Mahande¹¹, Blandina T. Mmbaga^1-3, Ali S. Khashan^5,12

Gaudensia A. Olomi ^1-3, Lottie G. Cansdale⁴, [...] Simon Woodworth^5,6, Rachel Manongi¹, Pendo Mlay^1,7, Karen Yeates⁸, Nicola West⁸, Jane E. Hirst^9,10, Christopher Redman¹⁰, Michael J. Mahande¹¹, Blandina T. Mmbaga^1-3, Ali S. Khashan^5,12

PUBLISHED 27 Nov 2023

Author details Author details

¹ Kilimanjaro Christian Medical University College, Moshi, Tanzania
² Regional Secretary of Kilimanjaro-Health Department, Kilimanjaro, Tanzania
³ Kilimanjaro Clinical Research Institution, Moshi, Kilimanjaro Region, Tanzania
⁴ Buckinghamshire Healthcare NHS Trust, Amersham, England, UK
⁵ INFANT Research Centre, University College Cork, Cork, County Cork, Ireland
⁶ Cork University Business School, University College Cork, Cork, County Cork, Ireland
⁷ Kilimanjaro Christian Medical Centre, Moshi, Tanzania
⁸ Department of Medicine, Queen's University, Kingston, Ontario, Canada
⁹ The George Institute for Global Health, Imperial College London, London, England, UK
¹⁰ Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, England, UK
¹¹ Department of Epidemiology & Biostatistics, Kilimanjaro Christian Medical University College, Moshi, Tanzania
¹² School of Public Health, University College Cork, Cork, County Cork, Ireland

Gaudensia A. Olomi
Roles: Conceptualization, Investigation, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Lottie G. Cansdale
Roles: Conceptualization, Investigation, Methodology, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Simon Woodworth
Roles: Conceptualization, Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Rachel Manongi
Roles: Conceptualization, Investigation, Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Pendo Mlay
Roles: Conceptualization, Investigation, Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Karen Yeates
Roles: Conceptualization, Investigation, Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Nicola West
Roles: Conceptualization, Writing – Review & Editing

Jane E. Hirst
Roles: Conceptualization, Investigation, Methodology, Writing – Review & Editing

Christopher Redman
Roles: Conceptualization, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

Michael J. Mahande
Roles: Conceptualization, Investigation, Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Blandina T. Mmbaga
Roles: Conceptualization, Investigation, Methodology, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Ali S. Khashan
Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background

Mobile health (mHealth) tools are increasingly being used in Sub-Saharan Africa (SSA) to improve the quality of health services. mHealth clinical decision-making tools have several established roles in maternal and perinatal healthcare including health surveillance, data collection and access to guidelines. The adoption of mHealth clinical decision-making tools in low-resource environments like SSA, as well as the lessons learnt from using them, have not yet been determined. As new mHealth technologies are quickly being evaluated and deployed in resource-poor settings, it is crucial to thoroughly analyse what has been accomplished in order to inform implementers and policy makers on the effectiveness of technology in evidence-based practice.

Objective

This study aims to synthesize the available evidence 1) on the use of mHealth clinical decision-making tools for maternal and perinatal care in SSA, and 2) whether these tools lead to improvements in the quality of maternal and perinatal care in SSA.

Methods

A systematic review of the literature will be performed to identify publications describing the use mHealth tools for maternal and perinatal clinical decision-making in SSA. PubMed, CINAHL, EMBASE, Global Health and Web of Science will be searched for relevant articles following a predetermined search strategy with no date restrictions. A limited grey literature search will also be carried out. Two independent reviewers will screen the articles. Pre-determined data items will be extracted, and data synthesis carried out using a descriptive approach. Appraisal will be done using the Appraisal of Guidelines Research and Evaluation Health Systems (AGREE-HS) instrument.

Conclusions

This systematic review protocol for identifying and appraising mHealth clinical decision-making tools in maternal and perinatal care may help to establish best practice for developing and scaling up, thus help to improve care in SSA.

Registration

PROSPERO (CRD42023452760; 19 August 2023).:

Keywords

mHealth, mobile health, maternal and perinatal care, clinical decision-making, Sub-Saharan Africa, Protocol

Corresponding author: Gaudensia A. Olomi

Competing interests: No competing interests were disclosed.

Grant information: The study was funded by an Irish Research Council (IRC), Department of Foreign Affairs COALESCE Award (COALESCE/2021/51). These funds were assigned to Doctor Ali S. Khashan as a Principal Investigator.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2023 Olomi GA et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Olomi GA, Cansdale LG, Woodworth S et al. mHealth clinical decision-making tools for maternal and perinatal health care in Sub-Saharan Africa: A systematic review protocol [version 1; peer review: 1 approved with reservations]. HRB Open Res 2023, 6:68 (https://doi.org/10.12688/hrbopenres.13799.1) First published: 27 Nov 2023, 6:68 (https://doi.org/10.12688/hrbopenres.13799.1) Latest published: 27 Nov 2023, 6:68 (https://doi.org/10.12688/hrbopenres.13799.1)

Introduction

Mobile health (mHealth) is an integral part of electronic health care, and refers to the use of mobile wireless technologies for health (Executive Board, 2018). Wireless mobile technologies are easy to use, widely acceptable and have a broad reach (Session, 2016). In 2021, the total number of mobile phones in SSA was 515 million, corresponding to 46% of the population having a mobile phone subscription (GSMA, 2022).

Maternal and perinatal mortality rates remain unacceptably high in many places, including Sub-Saharan Africa (Bossman et al., 2022). mHealth interventions have been recommended as one approach to counter this problem (Ruton et al., 2018). Their potential to enhance maternal and perinatal healthcare utilization, promote evidence-based practice and inform decision-making is supported by the near-universal availability of mobile phones (Kabongo et al., 2021). The identification of effective mHealth interventions in maternal and perinatal health could also contribute to accelerated progress towards achieving Sustainable Development Goal (SDG) 3, the promotion of good health and wellbeing, by 2030 (Bossman et al., 2022).

The quality of maternal and perinatal healthcare has been associated with the performance of healthcare workers. mHealth clinical decision-making tools have been reported to improve health workers’ performance (Abejirinde et al., 2018). The ability of these tools to improve healthcare has been attributed to its ability to increase data collection and surveillance, its use of decision algorithms and the increased access to evidence-based guidelines they can provide (Adepoju et al., 2017). mHealth can also offer a personalized communication tool to promote healthcare access and awareness (Ruton et al., 2018), and has been demonstrated to reduce patient waiting times, lower the cost of healthcare and support provision of systems for emergency response and monitoring (Sondaal et al., 2016).

Globally, there has been limited rigorous evaluation on the performance and effectiveness of mHealth interventions (Joos et al., 2016). Despite the potentially wide applicability of mHealth strategies and solutions to address patient and population needs, few pilot projects using mHealth have shown plans for scaling-up, and often do not integrate with existing health and information architecture (Executive Board, 2018). Lack of multisectoral involvement in development and implementation of mHealth tools also remains an issue (Executive Board, 2018). Government involvement in implementation and the integration of mHealth interventions into the existing healthcare system is encouraging and can pave the way to improved clinical decision-making by best practice implementation of mHealth interventions in SSA (Sondaal et al., 2016).

Evidence has shown that healthcare providers can be empowered to adopt and utilize mHealth tools when they are aligned to their needs, workload, training and skills (Abejirinde et al., 2018). mHealth interventions may be effective solutions to improve maternal and perinatal service utilization, but further studies assessing their impact on maternal and perinatal outcomes are needed (Sondaal et al., 2016).

Primary aim

The primary aim of this systematic review is to synthesize the available evidence on the use of mHealth clinical decision-making tools by healthcare workers for maternal and perinatal care, and whether these tools lead to improvements in the quality of maternal and perinatal care in SSA.

Objectives

1. To identify mHealth decision-making tools that are currently in use or have been piloted for use in maternal and perinatal care.
2. To describe the structure of the tools and identify data items collected by them.
3. To describe, if mentioned, facilitators and barriers to the implementation of these tools in maternal and perinatal care.
4. To identify if there is evidence to suggest that using mHealth tool help to improve clinical decision-making for maternal and perinatal care.

Review questions

1. What mHealth clinical decision-making tools have been reported to be used in maternal and perinatal care in Sub-Saharan Africa?
2. Are mHealth clinical decision-making tools effective in improving the quality of maternal and perinatal health care in Sub-Saharan Africa?

Protocol

Registration

This protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (protocol number CRD42023452760; 19 August 2023). It has been written in accordance with the reporting guidance provided in the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA-P) 2015 (Olomi et al., 2023b; Shamseer et al., 2015), and the full review will conform with the PRISMA guidelines on systematic reviews (Page et al., 2021).

Eligibility criteria

The eligibility of studies will be based on inclusion and exclusion criteria applied to the SPIDER framework (S: sample, PI: population of interest, D: study design, E: evaluation, R: research type), developed by Cooke et al., and outlined in Table 1. mHealth will be defined as the use of mobile wireless technologies for health (WHO, 2018)

Table 1. SPIDER framework for eligibility criteria.

mHealth, mobile health.

SPIDER framework	Eligibility criteria
S: Sample	- mHealth tools that support clinical decision-making for healthcare workers in maternal and perinatal healthcare. - Sub-Saharan African countries as defined by the World Bank 2023 (N=48).
PI: Phenomenon of Interest	- The mHealth tool to support clinical decision making in maternal and perinatal care.
D: Study Design	- Any study, report or other publication detailing a pilot or current use of an mHealth clinical decision- making tool for maternal and perinatal care. - Only English Language publications. - No date restriction.
E: Evaluation	1. What clinical decision is the tool designed to support? 2. Has the tool been tested in clinical practice, and if so how (study size, location, design, outcomes)? 3. Has the tool been tested in other populations from where it was developed? (i.e., external validation) 4. Is there evidence to support or refute whether the tool changed: a. Delivery of guideline-based care (i.e., quality of care) b. Costs of care c. Clinical endpoints 5. What are the facilitators and barriers to use of the tool?
R: Research type	- All relevant study types involving the use of mHealth clinical decision-making tool for maternal and perinatal care will be included, including both quantitative and qualitative studies. Only original articles will be included. - Grey literature including publications from national or international scientific societies, professional colleges, charitable organizations, and government organizations.

Search strategy

The following databases will be searched for relevant publications: PubMed, CINAHL, EMBASE, Global Health, and Web of Science. A search of the grey literature will be carried out.

The search strategy proposed can be found as Extended data (Olomi et al., 2023a) and includes suitable keywords relating to mHealth clinical decision-making tools. Date restrictions will not be applied as the term mHealth was not widely used before 2000. The search strategy was developed for a specific database and then adapted for use within other databases. An example of the search strategy developed for CINAHL is included in Table 2. Before the final searches are conducted, the search technique will be piloted.

Table 2. Sample database search developed for CINAHL.

CINAHL Subject Headings (MESH)	Other terms
Mobile applications; computers, handheld; cellular phone; telehealth	mobile phone* or mobile* or m-health* or mobile health* or mhealth* or m health* or electronic* or smartphone* or smart phone* or iPhone* or iPad* or tablet computer* or cellphone* or electronic health* or ehealth* or e-health* or cell phone* or handheld computer* or phone* or mobile device*
Maternal health services; prenatal care; perinatal care; postnatal care; pregnancy; obstetrics; delivery, obstetric;	Parturition* or childbirth* or labour* or maternal* or prenatal* or perinatal* or antenatal* or postnatal* or pregnancy* or maternal health* or obstetrics* or intrapartum* or antepartum* or postpartum*
Decision making; decision making, clinical; decision support systems, clinical;	Decision-making* or decision making* or decision-support* or decision support*
Africa South of the Sahara	Sub Saharan Africa* or sub-Saharan Africa* or Angola* or Benin* or Botswana* or Burkina Faso* or Burundi* or Cabo Verde* or Cameroon* or Central African Republic* or Chad* or Comoros* or Congo* or Cote D Ivoire* or Democratic Republic of the Congo* or Equatorial Guinea* or Eritrea* or Eswatini* or Ethiopia* or Gabon* or Gambia* or Ghana* or Guinea* or Guinea-Bissau* or Kenya* or Lesotho* or Liberia* or Madagascar* or Malawi* or Mali* or Mauritania* or Mauritius* or Mozambique* or Namibia* or Niger* or Nigeria* or Rwanda* or “Sao Tome and Principe”* or Senegal* or Seychelles* or Sierra Leone* or Somalia* or South Africa* or South Sudan* or Sudan* or Tanzania* or Togo* or Uganda* or Zambia* or Zimbabwe*

Study selection

Reviewers will examine titles and abstracts for the bibliographic database search against the inclusion and exclusion criteria listed below.

Inclusion criteria

1) Studies conducted in SSA, as defined by the World Bank.
2) mHealth tool must be either in use or have been piloted for use in maternal or perinatal care, at any level of health facility.
3) The data from the mHealth tool must be used to facilitate decision-making.
4) Articles published in English.
5) Original articles only.

Exclusion criteria

1) Articles reporting on mHealth clinical decision–making tools in healthcare areas outside maternal and perinatal health.
2) Studies not published in English.
3) Publication does not include the mHealth tool used for maternal and perinatal care, does not have enough relevant information on the tool in published material, or the tool cannot be accessed by other means (for example direct contact with the study authors, or the governing group).
4) Articles where the decision-making tool was used on a computer or desktop, as this falls out of the definition of mHealth.

Selection of studies for inclusion in the review

Results will be imported to EndNote and duplicates removed, before being uploaded to Rayyan software (Ouzzani et al., 2016). Two independent reviewers will initially screen the title and abstract of articles using the criteria outlined above. The full text of included publications will be retrieved and again screened by two independent reviewers. If consensus on eligibility is not reached, the opinion of a third reviewer will be sought. Citations of full-text articles included will also be searched for relevant literature, as well as the citations of systematic reviews yielded by our search strategy.

A PRISMA flow diagram will be used to illustrate the search process. The corresponding author of an eligible study who needs more information will be contacted through email. A reminder will be given two weeks after the deadline if the corresponding author doesn't respond.

Data collection

All related supplemental documentation will be downloaded by the reviewer before data extraction and quality evaluation after the mHealth tools for inclusion have been acquired. If the article, website, or other source does not contain links to these documents, a search will be done to find them. To verify completeness and proper document pairing, a second reviewer will check all supplemental documents.

Data extraction

Two impartial reviewers will extract the data and check it for accuracy and comprehensiveness. Data from publications included for full-text analysis will be extracted using a standardized, previously-tested form in Microsoft Excel. Disagreements will be settled by discussion and agreement. If agreement cannot be reached, a third reviewer's viewpoint will be requested. Any missing data from the mHealth tools will be noted in the data extraction form as "not described."

The following information will be extracted from identified mHealth clinical decision-making tools for maternal and perinatal care:

- Basic information (article title, authors, journal, year of publication, funding)

- Population (healthcare workers, number of pregnancies/women)

- Context (country, time period of data collection, number of sites, community/facility, rural/urban/referral hospital)

- Concept (who used technology, use of data)

- Technology (software name, developer, open source, local data storage, online data storage, who owns data)

- mERA items (infrastructure for mHealth, interoperability/HIS context, adoption inputs/training given prior to intervention) (Agarwal et al., 2016)

- Study methodology (type study, methods)

- Outcomes (feasibility, acceptability, user-satisfaction, adverse pregnancy outcomes)

Quality appraisal of included studies

The Cochrane Risk of Bias tool or the Newcastle Ottawa scale will be used to assess the quality of observational and randomised control trial studies.

Data synthesis

A descriptive method will be used in the data synthesis to evaluate and scrutinize the mHealth technologies discovered. A narrative explanation of the data that was retrieved will be provided, and there will be a special emphasis on the tools that have a section on developing suggestions based on the review findings (if applicable).

Discussion

The aim of this systematic review is to summarize the existing literature describing mHealth tools used for clinical decision-making in maternal and perinatal care. There is a need to identify what standardized mHealth tools could be used to improve clinical decision-making and subsequently quality of maternal and perinatal healthcare outcomes, particularly in Sub-Saharan Africa.

Potential strengths and limitations of this study

The use of a thorough search strategy, a prospectively registered protocol, adherence to the PRISMA guidelines, and provision of updated knowledge on the associations between mHealth clinical decision-making tools and the outcome of improved quality of maternal and perinatal care are some of the strengths of this systematic review. In order to reduce the possibility of reviewer-based bias, two reviewers will also do the data extraction and quality assessment of the included studies in addition to screening for study eligibility.

We believe that this review's potential limitations may be due to publication bias. Studies that demonstrate an effect are more likely to be published in English; but, due to resource constraints, this review is only able to focus on studies written in English, potentially ignoring pertinent studies written in other languages. The presence of publication bias will be evaluated, if possible, using a funnel plot.

Ethics and dissemination

There is no necessity for ethics approval because this is a procedure for a systematic review based on published data. The results are anticipated to be shared by presentation at pertinent professional and scientific conferences and publication in a peer-reviewed journal.

Study status

Currently, data extraction is being performed.

Data availability

Underlying data

No data are associated with this article.

Extended data

Figshare: Search Strategy. https://doi.org/10.6084/m9.figshare.24243376 (Olomi et al., 2023a).

Reporting guidelines

Figshare: PRISMA-P checklist for ‘mHealth clinical decision-making tools for maternal and perinatal health care in Sub-Saharan Africa: A systematic review protocol’. https://doi.org/10.6084/m9.figshare.24236827 (Olomi et al., 2023b).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Faculty Opinions recommended

References

Abejirinde IOO, Ilozumba O, Marchal B, et al.: Mobile health and the performance of maternal health care workers in low- and middle-income countries: A realist review. Int J Care Coord. 2018; 21(3): 73–86. PubMed Abstract | Publisher Full Text | Free Full Text
Adepoju IOO, Albersen BJA, De Brouwere V, et al.: mhealth for clinical decision-making in sub-saharan africa: A scoping review. JMIR Mhealth Uhealth. 2017; 5(3): e38. PubMed Abstract | Publisher Full Text | Free Full Text
Agarwal S, LeFevre AE, Lee J, et al.: Guidelines for reporting of health interventions using mobile phones: Mobile health (mHealth) Evidence reporting and assessment (mERA) checklist. BMJ. 2016; 352: i1174. PubMed Abstract | Publisher Full Text
Bossman E, Johansen MA, Zanaboni P: mHealth interventions to reduce maternal and child mortality in Sub-Saharan Africa and Southern Asia: A systematic literature review. Front Glob Womens Health. 2022; 3: 942146. PubMed Abstract | Publisher Full Text | Free Full Text
Executive Board: mHealth: use of appropriate digital technologies for public health: report by the Director-General. World Health Organization, 2018; 10(1). Reference Source
GSMA: The mobile money Sub-Saharan Africa 2022, The mobile market in numbers. 2022; 38.
Joos O, Silva R, Amouzou A, et al.: Evaluation of a mhealth data quality intervention to improve documentation of pregnancy outcomes by health surveillance assistants in Malawi: A Cluster randomized trial. PLoS One. 2016; 11(1): e0145238. PubMed Abstract | Publisher Full Text | Free Full Text
Kabongo EM, Mukumbang FC, Delobelle P, et al.: Explaining the impact of mHealth on maternal and child health care in low- and middle-income countries: a realist synthesis. BMC Pregnancy Childbirth. 2021; 21(1): 196. PubMed Abstract | Publisher Full Text | Free Full Text
Olomi GA, Cansdale L, Woodworth S, et al.: Search Strategy. figshare. [Dataset], 2023a. http://www.doi.org/10.6084/m9.figshare.24243376.v1
Olomi G, Khashan AS, Mmbaga BT, et al.: PRISMA-P Checklist. figshare. [Dataset], 2023b. http://www.doi.org/10.6084/m9.figshare.24236827.v1
Ouzzani M, Hammady H, Fedorowicz Z, et al.: Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016; 5(1): 210. PubMed Abstract | Publisher Full Text | Free Full Text
Page MJ, McKenzie JE, Bossuyt PM, et al.: The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ. 2021; 372: n71. PubMed Abstract | Publisher Full Text | Free Full Text
Ruton H, Musabyimana A, Gaju E, et al.: The impact of an mHealth monitoring system on health care utilization by mothers and children: An evaluation using routine health information in Rwanda. Health Policy Plan. 2018; 33(8): 920–927. PubMed Abstract | Publisher Full Text | Free Full Text
Shamseer L, Moher D, Clarke M, et al.: Preferred reporting items for systematic review and meta-analysis protocols (prisma-p) 2015: Elaboration and explanation. BMJ. 2015; 350: g7647. PubMed Abstract | Publisher Full Text
Sondaal SFV, Browne JL, Amoakoh-Coleman M, et al.: Assessing the effect of mHealth interventions in improving maternal and neonatal care in low- And middle-income countries: A systematic review. PLoS One. 2016; 11(5): e0154664. PubMed Abstract | Publisher Full Text | Free Full Text
The 138th session: World Health Organization 138 Th Session. 2016; 25–30. Reference Source
WHO: Use of appropriate digital technologies for public health. 2018; [Accessed on 16 Sept.2023]. Reference Source

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