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Abstract
Introduction Recently, there has been a steady increase in mobile health (mHealth) interventions aimed at improving maternal health of women in low-income and middle-income countries. While there is evidence indicating that these interventions contribute to improvements in maternal health outcomes, other studies indicate inconclusive results. This uncertainty has raised additional questions, one of which pertains to the role of targeting strategies in implementing mHealth interventions and the focus on pregnant women and health workers as target groups. This review aims to assess who is targeted in different mHealth interventions and the importance of targeting strategies in maternal mHealth interventions.
Methods and analysis We will search for peer-reviewed, English-language literature published between 1999 and July 2017 in PubMed, Web of Knowledge (Science Direct, EMBASE) and Cochrane Central Registers of Controlled Trials. The study scope is defined by the Population, Intervention, Comparison and Outcomes framework: P, community members with maternal or reproductive needs; I, electronic health or mHealth programmes geared at improving maternal or reproductive health; C, other non-electronic health or mHealth-based interventions; O, maternal health measures including family planning, antenatal care attendance, health facility delivery and postnatal care attendance.
Ethics and dissemination This study is a review of already published or publicly available data and needs no ethical approval. Review results will be published in a peer-reviewed journal and presented at international conferences.
PROSPERO registration number CRD42017072280.
- reproductive health
- mobile health
- low and middle income countries
- reproductive medicine
- maternal health
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
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Strengths and limitations of this study
The protocol adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Protocols guidelines for reporting a systematic review protocol.
The protocol outlines a review process that will involve the use of a systematic literature review, an interdisciplinary team and a narrative synthesis methodology, allowing for an iterative review process.
The proposed inclusion criteria include quantitative and qualitative studies and a narrative synthesis methodology. This combination presents an opportunity for the review to answer questions not only about ‘what’ “but also about ‘why and how’.
The protocol proposes the utilisation of multiple tools to assess the strength of evidence including the Downs and Black (1998) checklist for quantitative healthcare studies and the Grading of Recommendations Assessment, Development and Evaluation system.
Narrative synthesis as a form of content methodology has been criticised for its potential to be biased, and its transparency has been challenged. We address these by using a systematic four-step approach to this synthesis.
Introduction
Mobile health (mHealth) involves the use of mobile phones or portable devices such as personal digital assistants (PDAs) for healthcare service delivery. These interventions are usually in the form of direct phone calls, short message service (SMS) messages, voice calls or mobile applications.1 mHealth interventions have increasingly been used in improving maternal health outcomes. The range of available mHealth interventions resulted in Labrique et al developing a framework which identified the 12 common uses of mHealth for maternal and child health, the most predominant being its use for client education and behaviour-change communication.2 These interventions may target the supply end of care delivery, aimed at health workers at the facility level, or individual and community levels, that is, the demand-side influencers such as household decision-makers. In this review, we will focus on the individual-level interventions which have focused on strategies shown to work for maternal health including increased delivery assistance by a skilled birth attendant, attendance of four or more antenatal care (ANC) visits, and an increased prevalence of contraceptive usage among reproductive-aged women.3
mHealth studies which have focused on maternal interventions have yielded conflicting results. There is some positive evidence for the effect on mHealth interventions on outcomes such as ANC attendance and skilled birth attendance at birth and utilisation of oral contraception.4–7 However, other studies have reported no statistically significant improvement in reproductive health outcomes following the introduction of mHealth interventions.8–11 With the interest in mHealth interventions, there have been a number of reviews on the use of mHealth in low-income and middle-income countries (LMIC). However, they tend to focus specifically on the reproductive health outcomes or the efficacy of mHealth interventions,12–14 the use of mHealth by health workers15–17 or the acceptance, utilisation and evaluation of these interventions.18–20 None of these reviews have focused on the targeting strategies of reproductive mHealth interventions, that is, who are the intervention targets and to what extent this influences observed outcomes.
Literature on maternal health provides strong evidence for the targeting of individuals other than pregnant women. For example, there is evidence that in many contexts husbands play significant and important roles in reproductive health decision-making.21 22 This variation in decision-makers is rarely addressed and sometimes strategically excluded from the review, as in Lee et al.12 In addition, other factors that influence reproductive health decision-making and healthcare-seeking behaviour such as literacy, socioeconomic status or access to care are discussed as secondary findings. As a result, there is as yet not much of an understanding regarding whom mHealth interventions are to be designed for, and the relation between targeting strategies in mHealth interventions and reproductive health outcomes.
Objective and research questions
The aim of this review is to understand the effects of targeting strategies applied in mHealth interventions for maternal health in LMIC. To address this aim, the following research questions were developed:
Who in the community (ie, non-health professionals) a targeted in maternal mHealth interventions?
Do targeting strategies differ across LMIC contexts, such as by programme type, funder and so on?
What are the sociodemographic characteristics of those targeted in the described mHealth interventions, and what is the nature of mHealth interventions used for these groups?
What are the reported intervention outcomes based on characteristics of targeted participants and how are these differences explained?
Methods and analysis
Protocol registration
The review protocol was preregistered in the PROSPERO database (CRD42017072280). This protocol adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Protocols 2015 guidelines for preparing protocols of systematic reviews23 (online supplementary material).
Supplementary file 1
Study design
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) will guide the review.
Study eligibility
Eligible studies will include:
randomised and non-randomised controlled trials
cross-sectional
longitudinal studies
case studies
ethnographies and other types of qualitative research (eg, grounded theory, action research)
systematic review and meta-analyses.
Eligible papers will address the question of ‘who in the community are targeted in mHealth intervention?’ Studies will be selected if mHealth interventions fit the definition of mHealth by Robert et al 1: ‘mobile computing, medical sensor, and communications technologies for health care’.24 Furthermore, studies will need to be captured within one of the 12 common ICT application frameworks for maternal health proposed by Labrique et al.25 These criteria are further discussed in the following section on study inclusion and exclusion.
Inclusion/exclusion
The participants, interventions, comparators and outcomes for this review are:
Participants (P)
All individuals or groups within communities (eg, husbands/spouses, mothers-in-law, family members, household members) (excluding health workers) who have been involved in a mHealth intervention geared at improving maternal health knowledge, behaviours and outcomes. Participants will be limited to those in LMIC as defined by the World Bank’s classification scheme.26
Interventions (I)
mHealth interventions including SMS or text messages, mobile applications and phone calls which address maternal health issues, such as attendance at ANC visits, receipt and consumption of folic acid tablets, delivering with a skilled birth attendant or at a health institution, use of family-planning services, will be included. Health technologies will also include unidirectional or multidirectional messaging services (SMS or voice messages). Applications installed on patient smartphones/tablets or PDAs or those used by health workers, to promote maternal health-seeking behaviour in (pregnant) women or women of reproductive age, for which maternal health outcomes were measured. mHealth interventions that include women as direct end-users, alongside health workers or professionals for which maternal health outcomes were measured, will also be included.
In table 1 we present an overview of the 12 common mHealth and information communication technology applications based on Labrique et al,25 examples of mobile phone functions and our decision to include or exclude. For example, the first group of applications is for use in client education and behaviour-change communication. When these are used among participants of interest in the review, this category is applicable and thus included. Conversely, electronic health records are not applicable to our participants and thus such applications will be excluded.
Intervention inclusion and exclusion list based on 12 common applications and a visual framework (Labrique et al)25
Comparators (C)
Control groups may have received either the standard of care or other maternal interventions without an mHealth component.
Outcomes (O)
Four primary maternal health outcomes will be identified (a) knowledge-related outcomes, including knowledge of required of ANC visits, danger signs during pregnancy and delivery, appropriate contraceptive use; (b) attitudinal changes, such as increased willingness to attend ANC and motivation; (c) perceptions of recommended maternal health and family planning behaviours and quality of care; (d) change in maternal health-seeking behaviours or family planning practices, such as increased attendance at antenatal clinics, delivery at health facilities and utilisation of family-planning method. For our review, we have adopted an expanded definition of maternal health which includes family planning as a key component in reducing maternal mortality.
Exclusion criteria
Studies will be excluded if they focus on outcomes not directly related to maternal health as defined earlier. This includes studies on sexually transmitted infections such as chlamydia or HIV. Studies on specific subpopulations such as sex workers will also be excluded. Protocols for research studies or reviews will be excluded.
Search criteria
Searches will be conducted on articles published between 1999 and July 2017, to capture all articles since the emergence of mHealth technology in the health literature.27
We will search the following databases: PubMed, Web of Knowledge (Science Direct, EMBASE) and Cochrane Central Registers of Controlled Trials. Reference lists of included articles will also be reviewed for other relevant articles for inclusion. We will also search other databases for grey literature including ‘mHealth Alliance’ and ‘mHealth Evidence’.28 Searches will be limited to English articles. Search terms will consist of Medical Subject Headings (MeSH), title/abstract (tiab) and text words (tw). Search terms will focus on mHealth, LMIC and maternal health. The proposed strategy can be found in table 2.
PubMed search strategy, to be adapted for use in other database searches
Selection of studies
After the search strategy has been finalised and run on all search engines, they will be extracted. All extracted articles will be stored and managed in Mendeley reference manager. Duplicates will be identified and deleted using Mendeley duplicate identification tools. First-level inclusion: OI and I-OOA will conduct title and abstract screening of articles based on the inclusion and exclusion criteria. Second-level inclusion: full-text reading of all included articles by OI and I-OOA. SVB and MD will make the final decisions whenever the first two authors are unable to reach a consensus. Reasons for decisions taken at both levels of inclusion will be noted in an Excel sheet. The inclusion and exclusion processes will also be reported in a PRISMA flow chart.23
Data extraction
OI will develop a data-extraction form to be used throughout the review process. OI and I-OOA will pilot the form on a subset of 10 articles to assess the functionality and suitability of the form. Discussions will be held with the entire review team (OI, I-OOA, SVB, AB, JEWB and MD) and adjustments made to the form before data extraction commences. Key information to be extracted can be found in table 3.
Components of data-extraction form
Data synthesis
Extracted data will be summarised using narrative synthesis (NS) as described by Popay et al. 29 NS primarily involves the usage of words and text to summarise review findings. NS has four key elements which we present below29 :
Developing a theory of how the intervention works, why and for whom: there are no specific tools recommended for the design of a programme theory. The development of a programme theory will be guided by findings from the other three elements of an NS.
Developing a preliminary synthesis of findings of included studies: tabulation will be used to represent the quantitative and qualitative studies included in the review. This will include information on the study design, participant characteristics, intervention details, setting/context and outcome measures.
Exploring relationships in the data: conceptual/thematic models/groups will be developed across and within groups of similar articles. These groups would allow for the identification of similar findings.
Assessing the robustness of the synthesis: quality of the synthesis will be assessed through a critical reflection on the synthesis process. This will be presented in the Discussion section of the systematic review and will include reflection on the NS methodology, reviewers’ assumptions and factors that could have influenced review findings.
Despite the linear presentation of the elements, the synthesis process is often iterative. A synthesis process flow chart will be included in the systematic review to display the process.
Missing data
When data is missing from articles and not publicly available, attempts will be made to contact study authors directly for the resource.
Quality of evidence
Quality of evidence will be assessed using method-appropriate tools. The Downs and Black30 checklist for quantitative healthcare studies will be used for all quantitative studies. This checklist was chosen for its multiple advantages, including its usefulness for both randomised controlled studies and non-randomised control studies and its being tailored for healthcare interventions. Quality appraisal for qualitative studies will be conducted using the guidelines proposed by Mays and Pope.31 These include questions about the worth and relevance of the work, clarity of research questions, appropriateness of the study design to the question, study context, sample and data collection, and analysis. If possible, overall quality of studies will be evaluated using the Grading of Recommendations Assessment, Development and Evaluation framework for quality of evidence.32 33 Quality-appraisal tables and figures will be presented as additional files.
Ethics and dissemination
The sustained and increasing interest in mHealth over the last decades has led to an increase in mHealth interventions in reproductive health in LMIC. However, it is not clear what the role of targeting strategies is in the implementation of interventions (and the achievement of outcomes) and how these are related to reproductive health decision-making roles in different contexts. An understanding of this question is fundamental in future design and implementation of maternal and reproductive mHealth, and potentially other mHealth interventions in LMIC.
Acknowledgments
The authors would like to thank Ralph de Vries, Vrije Universiteit Library, for his assistance in developing and refining the search strategy.
References
Footnotes
Contributors OI, I-OOA, MD and SVB conceived the review design and participated in refining the manuscript. AB and JEWB contributed to refining the review design and OI drafted the manuscript. All authors read and approved the final manuscript.
Funding This work was supported by Erasmus Mundus Joint Doctorate Fellowship, Framework Partnership Agreement 2013-0039, Specific Grant Agreement 2014-0681.
Disclaimer The funder had no role in the decision to write and publish this protocol.
Competing interests None declared.
Patient consent Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Collaborators Ralph de Vries.