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Low- and middle-income countries (LMICs) face the highest burden of maternal and neonatal deaths. Concurrently, they have the lowest number of physicians. Innovative methods such as the exchange of health-related information using mobile devices (mHealth) may support health care workers in the provision of antenatal, delivery, and postnatal care to improve maternal and neonatal outcomes in LMICs.
We conducted a systematic review evaluating the effectiveness of mHealth interventions targeting health care workers to improve maternal and neonatal outcomes in LMIC.
The Cochrane Library, PubMed, EMBASE, Global Health Library, and Popline were searched using predetermined search and indexing terms. Quality assessment was performed using an adapted Cochrane Risk of Bias Tool. A strength, weakness, opportunity, and threat analysis was performed for each included paper.
A total of 19 studies were included for this systematic review, 10 intervention and 9 descriptive studies. mHealth interventions were used as communication, data collection, or educational tool by health care providers primarily at the community level in the provision of antenatal, delivery, and postnatal care. Interventions were used to track pregnant women to improve antenatal and delivery care, as well as facilitate referrals. None of the studies directly assessed the effect of mHealth on maternal and neonatal mortality. Challenges of mHealth interventions to assist health care workers consisted mainly of technical problems, such as mobile network coverage, internet access, electricity access, and maintenance of mobile phones.
mHealth interventions targeting health care workers have the potential to improve maternal and neonatal health services in LMICs. However, there is a gap in the knowledge whether mHealth interventions directly affect maternal and neonatal outcomes and future research should employ experimental designs with relevant outcome measures to address this gap.
The risk for maternal or newborn death is considerably higher in low- and middle-income countries (LMICs) as compared with high-income countries. Despite progress with global decline in maternal mortality, many LMICs still have high maternal mortality rates [
High neonatal mortality rate particularly persists in LMICs [
A potential tool to address maternal and neonatal outcome in LMICs is provided by the global increase in mobile technology. The International Telecommunication Union reported that in 2013, global mobile-phone subscriptions reached 6.8 billion and that the mobile-cellular penetration rate or the number of active mobile phone users within a specific population reached 89% in developing countries [
The current systematic review is based on the guidelines provided by PRISMA [
An electronic systematic literature search was conducted within the following 5 databases: The Cochrane Library (Cochrane Database of Systemic Reviews), PubMed or MEDLINE, EMBASE, Global Health Library, and POPLINE using predefined search terms (Title or Abstract) and indexing terms (MeSH, Emtree) during the period of June 1, 2014, and August 31, 2014. In addition, Grey literature search was performed between October 2014 and April 2015 because many studies focusing on mHealth interventions are not published in peer-reviewed journals. A list was created of organizations working with mHealth interventions. These organizations consisted of nongovernmental organizations, governments’ agencies, and the World Health Organization working group on mHealth (
Studies focusing on the domain health care workers in combination with maternal and neonatal care in LMICs were eligible for inclusion. The list of LMICs was created according to the World Bank Classification [
Included papers were all peer-reviewed, written in English, Dutch, French, German, or Spanish, and primary study papers. Papers were excluded when they did not match the domains and determinants, or were reports of proceedings, project protocols, secondary analysis, animal, biomolecular, or genetic studies. Citations of secondary analysis were reviewed for relevant citations. Interventions relating to the termination of pregnancy were excluded when they targeted the termination of pregnancy before 26-week gestation, as the fetus is then not yet regarded as viable. Interventions making use of a radio were excluded because these interventions fell outside the scope of our definition of mHealth.
The database searches were carried out by ABB and SFS. Subsequent review of search results was undertaken by ABB, MAC, SFS, JB, and KKG. Three reviewers (ABB, ASM, and MV) screened the papers found in the grey literature search. There were no disagreements on paper inclusion.
Data extraction was done according to a standardized data extraction form based on: the study, study design, location, target population or size, form of mHealth, focus of evaluation measure (whether maternal or neonatal), mHealth function, relevant study findings with respect to outcome used in the study, role of mHealth, and the strengths, weaknesses, opportunities, and threats of the intervention.
Extraction of the data from database papers was done by a single reviewer (ABB) who was not blinded for journal or author details. Lack of clarity during the extraction process was resolved by consulting the second reviewer (MAC). Data extraction of the grey literature was done by 4 reviewers (ABB, ASM, MV, and MAC). In case of incomplete data, one attempt was made to contact the corresponding author by email.
The quality of the included papers was assessed according to an adapted Cochrane Risk of Bias Tool [
Studies were grouped into 2 types: intervention and descriptive. Intervention studies employed more rigorous nonrandomized study designs used for evaluating interventions [
Narrative synthesis of the intervention studies are presented in an evidence table, in which the studies are analyzed according to their year of publication, study design, location or setting, target population, whether evaluation measures are maternal or neonatal, form of mHealth, mHealth functions related to data collection, educational, and communication and finally relevant findings. A similar evidence table was used to summarize the findings of the descriptive studies. Heterogeneous outcomes, settings, and varying study designs limited our ability to group the results of 2 or more papers together to conduct a meta-analysis for an overall quantitative conclusion. A strengths, weaknesses, opportunities, and threat analysis was also performed for all the included studies, as well as for mHealth as an intervention.
A total of 3725 papers were identified in the database and grey literature searches. After removal of duplicates using Endnote (version 11), 2965 articles remained and were screened by title and abstract. This resulted in exclusion of 2909 articles, leaving 56 articles to screen for eligibility. Thirty-seven articles were further excluded. Reasons for exclusion included unavailability of full text (n=17), language (n=2), secondary analysis (n=8), reports (n=7), and unavailability of records providing additional but key information on studies (n=3). A total of 19 articles were included in our study, 10 intervention studies and 9 descriptive studies.
Regarding the quality of mHealth evaluations in the studies, only one of the intervention studies was a randomized controlled trial (RCT) [
Scope of studies included in the review.
Category | Subcategory | Intervention studies (N=10) | Descriptive studies (N=9) | ||
Number of studies | % of studies | Number of studies | % of studies | ||
Africa | 8 | 80.0 | 6 | 66.7 | |
Asia | 2 | 20.0 | 3 | 33.3 | |
Unidirectional text messaging | 3 | 30.0 | 3 | 33.3 | |
Multidirectional text messaging | 2 | 20.0 | 1 | 11.1 | |
Multidirectional text and voice messages | 1 | 10.0 | 1 | 11.1 | |
Unidirectional text messaging and Web-based technology | 1 | 10.0 | 1 | 11.1 | |
Mobile phone health apps or surveys | 2 | 20.0 | 2 | 22.2 | |
Mobile phone software | - | - | 1 | 11.1 | |
Mobile phone recording | 1 | 10.0 | - | - | |
Data collection | 6 | 60.0 | 4 | 44.4 | |
Educational | 1 | 10.0 | 3 | 33.3 | |
Communication or information sharing | 5 | 50.0 | 2 | 22.2 | |
Postpartum hemorrhage | 1 | 10.0 | |||
Skilled maternal and newborn care | 4 | 40.0 | 2 | 22.2 | |
Training or educating midwives and nurses | 2 | 20.0 | 4 | 44.4 | |
Reproductive health | 1 | 10.0 | - | - | |
HIV and pregnancy | 2 | 20.0 | 1 | 11.1 | |
Malaria in pregnancy | - | - | 1 | 11.1 | |
Postnatal depression | - | - | 1 | 11.1 | |
Infant feeding | 1 | 10.0 | - | - | |
Traditional birth attendants | 2 | 20.0 | 1 | 11.1 | |
Health extension workers | 1 | 10.0 | 1 | 11.1 | |
Midwives | 2 | 20.0 | 2 | 22.2 | |
Health care staff | 2 | 20.0 | 2 | 22.2 | |
Medical students | 1 | 10.0 | - | - | |
Community health workers | 3 | 30.0 | 4 | 44.4 | |
Health surveillance assistants | 1 | 10.0 | - | - |
PRISMA flow diagram of studies included in this review.
The overall risk of bias assessment is reported in
Graphical presentation of risk of bias assessment for intervention studies included in the review.
The intervention studies distinguish the use of mHealth directed at health care providers for data collection, communication, or educational purposes.
Six papers described an mHealth intervention used as a data collection tool [
Two of these studies assessed the knowledge and skill retention of midwives and TBAs after training sessions on how to use mobile phones as data collection tool [
In a study conducted in Ethiopia, most health workers were able to use mobile phone health apps that are appropriate for their technical needs in terms of maternal health data collection [
Three studies assessed the use of mHealth interventions as a communication tool [
SMS texting between CHWs and either ambulance, health facility staff, district hospital, and central level, enabled an effective and real-time 2-way communication alert system to reduce maternal and child health deaths in Rwanda [
Characteristics of the intervention studies included in the review and the relevant findings.
Study/ (Focus of evaluation) | Study design | Target population or size (Setting) | Form of mHealth | mHealth function | Relevant findings |
2014, Munro et al [ |
Nonrandomized design (preintervention and postintervention evaluation) | 99 TBAsa trained; 63 retained 1-year posttraining for complete evaluation |
Utilization of mobile phone functions, coded SMS text messaging | Data collection and transmission (locating pregnant women; take data on age and referring them for antenatal care) | Participants demonstrated an increase in the mean number of skills that they were able to perform between pretest and both the immediate posttest and 1-year posttest. |
2014, Pathfinder (Grey Literature) [ |
Nonrandomized design (preintervention and postintervention evaluation) | 258 participants in an infant feeding health education program |
150 CHWc in 10 primary health clinics | Education and communication (ANCd protocols, and client follow-up) | CHWs increased HIVe testing from 68% to 82%. |
2013, Little et al [ |
Nonrandomized design (preintervention and postintervention evaluation | 20 HEWf, 12 midwives, 5 supervisors (Kilte and Awelalo districts in the Tigray region of Ethiopia) | Mobile phone app using open source components | Data collection (using appropriate technologies to meet needs of HEW and midwives | GPRSg connection was available in 35 health posts and centers (74%) of the study districts. |
2012, Zhang et al [ |
Randomized controlled trial | 10 students of the Hebei Union School of Public Health |
Mobile phone data collection | Data collection (Use of mobile phones for data collection on infant feeding practices compared with use of pen and paper) | In 120 copies of pen-and-paper questionnaires, 55 questionnaires contained errors. |
2012, Lori et al [ |
Nonrandomized design (preintervention and postintervention evaluation) | 99 TBAs |
SMS text messaging | Data collection (using a pregnancy reporting protocol) | Mean increase in mobile phone knowledge scores was 3.67 (95% CI 3.39-3.95). |
2012, Seidenberg et al [ |
Nonrandomized design (preintervention and postintervention evaluation) | At least 2 health workers from each facility |
SMS text messaging | Data collection and transmission (to reduce the time between blood sampling for the detection of infant HIV infection and notification of the test results to the relevant point-of-care health facility by using SMS-based system | Mean turnaround time for delivery of a test result to the relevant health facility fell from 44.2 days (SDh:28) preimplementation to 26.7 days (SD:31.8) postimplementation. |
2012, Lemay et al [ |
Nonrandomized controlled trial (staged design) | Health surveillance assistants and community health workers. 95 SMS users in Salima. 95 nonusers in Salima. 95 nonusers in Kasungu |
SMS text messaging | Communication (reducing communication gaps between health workers and their district teams; increasing access to information and improve quality of services) | SMS used to report stock-outs, asking general information, reporting emergencies, confirming meetings, and requesting technical support. |
2012, Ngabo et al [ |
Nonrandomized design (pre intervention and postintervention evaluation) | 432 community health workers and the rest of the health system (Musanze, Rwanda) | SMS Text messaging (Rapid SMS-MCHi system) | Communication (SMS-based platform, enabling effective and real time 2-way communication for action, between CHWs at community level, and the rest of the health system. Used to improve access to antenatal, postnatal care, institutional delivery, and emergency obstetric care) | 5734 SMS were sent. |
2011, Andreatta et al [ |
Nonrandomized design (posttraining evaluation) | 8 TBA, 2 professional nurse midwives (Sene District in Ghana) | SMS text messaging | Data collection and communication (reporting postpartum hemorrhage occurrence, management, and outcome) | Both professionals and TBAs were able to use the specified reporting and text messaging protocols to report clinical outcomes. |
2010, Kwaewkungwal et al [ |
Nonrandomized design (preintervention and postintervention evaluation) | Health workers in charge of ANC or EPIk services (sample size not indicated in paper) (Phung district, Thai-Myanmar) | SMS text messaging and Web-based apps | Data collection, automated generation of list, and update information regarding the antenatal care and child's immunization status on mobile phone when performing ANC or EPI activities off health care clinic | 59% come on time as per scheduled dates after implementation compared with 44% before implementation. |
aTBA: traditional birth attendant.
bSMS: short message service.
cCHW: community health worker.
dANC: antenatal care visit.
eHIV: human immunodeficiency virus.
fHEW: health extension worker.
gGPRS: general packet radio service.
hSD: standard deviation.
iMCH: maternal and child health.
jPPH: postpartum hemorrhage.
kEPI: expanded program on immunization.
In Indonesia, a theoretical model on the use of mobile phones to enhance the capacity of health workers was developed and tested among 223 midwives [
A Strengths, Weaknesses, Opportunities, and Threat analysis was conducted for all included studies. All studies included are relatively current studies published from 2010 onward providing up-to-date information. There is also a good variation of settings within the domain of LMIC, with studies conducted in West, East, and Southern Africa, and different parts of Asia. All forms of mHealth interventions as well as different functions that can be served by mHealth are represented in these studies. In all but one study, standardized phones were procured for participants. Strength of the included studies is the broad range of health worker categories considered, allowing for easy assessment of feasibility of mHealth apps for the daily work of health care workers. Weaknesses in the studies related mainly to their study design. Only one of the intervention studies was a RCT [
Multiple studies mention low costs to be strength of the mHealth interventions compared with traditional methods [
Weaknesses included that the information in the text messages was too simple and needed additional detailed information [
Clear opportunities exist for utilization of mHealth. This includes the additional functions of the technology, such as global positioning system, taking and storing pictures and videos, as well as the ability to record sound. These can facilitate data collection tools in the future [
Factors that threaten mHealth implementation included lack of reliable Web coverage, which limits the potential of mHealth in the public sector [
Characteristics of the descriptive studies included in the review and the relevant findings.
Study |
Study design | Target health workers or size |
Form of mHealth | mHealth function | Relevant findings |
2014, Lee et al [ |
Cross-sectional | 223 midwives |
One-way mobile phone use | Improving access to health-related resources: formal (medical professionals) and informal (peer workers) resources | Mobile phone use was positively associated with midwives' access to institutional and peer information resources. |
2014, Tsai et al [ |
2 Cross-sectional studies | Community health workers (sample size not stated in paper) (Khayelitsha Cape Town, South Africa) | Mobile phone program | Case finding (use of mobile phones for administering the EPDSa during the routine course of their community-based outreach and wellness work | CHWsb were able to detect probable antenatal depression using the scale during their routine outreaches with excellent discrimination, with area under the receiver-operating characteristic curve (AUC) values ranging from 0.91 to 0.99; 0.97 sensitivity and 0.76 specificity. |
2014, Pimmer et al [ |
Case study | 16 nurses attending an advanced midwifery course |
Mobile phones | Nurse education (mobile phones as educational tools) | Nursing students in resource poor settings use mobile technology as educational tools. |
2014, D-Tree International [ |
Cross-sectional | 24 TBAs in phase I and 223 CHWs in phase II |
Mobile phone with open source mobile app | Data collection and communication and information sharing | There was an increase in access to skilled care during pregnancy, childbirth, and post-partum care. |
2014, World Vision [ |
Cross-sectional | CHWs (sample size not provided in paper) |
Mobile phone | Counseling and referrals | Promotion of health facility deliveries. |
2013, van Heerden et al [ |
Cross-sectional | 16 data collectors; |
MPAPId | Data collection (feasibility of face-to-face maternal health data collection from pregnant women living with HIV using a mobile phone survey app) | Perceived usefulness was reported to be slightly higher than perceived ease of use. |
2013, IICDg [ |
Cross-sectional | 50 CHWs and 10 health specialists (Yirimodjo, Mali) | MAMMAh | Data collection and monitoring (questionnaire with malaria indicators and monitoring of disease evolution) | 31% reduction in malaria in pregnancy cases. |
2012, Woods et al [ |
Cross-sectional | 50 midwives out of 2500 midwives from public and private sectors |
SMS text messaging; with link to a website with additional information | Education | 86% enjoyed and learned from weekly text messages. |
2010, Alam et al [ |
Case study | 9 BRACi health workers |
Mobile phones with smart algorithms (The Click Module) | Data collection (real-time access to data) | Health workers could send data directly to the central MISj system. |
aEPDS: Edinburgh Postnatal Depression Scale.
bCHWs: community health workers.
cDHS: demographic and health survey.
dMPAPI: mobile phone-assisted personal interviewing.
eHIV: human immune deficiency virus.
fMLH: mothers living with HIV.
gIICD: International Institute for Communication and Development.
hMAMMA: Mamans Mobiles contre le Malaria au Mali.
iBRAC: building resources across community (a nongovernmental organization).
jMIS: management information system.
This systematic review shows effective use of mHealth interventions as communication, educational, and data collection tools by health workers to report on medical events related to maternal and child health within their community. These constitute health systems strengthening app tools [
The fact that most of the studies included in this review targeted health workers at community level provides insights into the possibility of creating an intermediate layer in which health workers form an important linkage between higher health institutions and the community in harnessing the befits of mHealth.
Some challenges of mHealth were identified, and these were mainly technological problems, such as mobile network coverage, Web-based access, electricity access, and maintenance of mobile phones [
Given the large investments in mHealth [
Limitations of our review include a high risk of bias observed for some of the intervention studies, mainly relating to limited consideration of confounding. Only one study was a RCT. Most studies were pilot or implementation studies. A further limitation of the current systematic review is the domain limitation of LMIC. This affects the generalizability of our results to other settings, as we are aware that some studies in high-income countries or low-income mothers in high-income countries could provide informative insight in the effectiveness of mHealth interventions to improve health outcomes [
The strength of the current systematic review is the comprehensive search conducted including available grey literature reflecting current activities of nongovernmental organizations, which are often not published in peer-reviewed journals. This paper thus provides a comprehensive overview of the available literature on the effectiveness of mHealth interventions to date and narratively assesses the broad function of mHealth. The methodology used in the narrative synthesis looks at the broad function of mHealth as used in the study, the targeted frontline providers, and the effectiveness of the mHealth intervention, an approach that facilitated easy assessment of the usefulness of the various mHealth functions.
This systematic review indicates that mHealth interventions targeting health care workers have the potential to materially improve maternal and neonatal health services in LMICs. There is, however, a gap in the knowledge of how mHealth interventions directly affect maternal and neonatal outcomes and future research should employ experimental designs to address this gap.
List of organizations contacted for grey literature.
Full search strategy.
Adapted quality assessment tool.
Table showing Risk of Bias assessment for included intervention studies.
community health worker
human immunodeficiency virus
low- and middle-income country
randomized controlled trial
short messaging service
traditional birth attendant
MAC gratefully acknowledge technical support from the Julius Center for Health Sciences and Primary Care. The authors also thank the Netherlands Organization for Scientific Research (NWO) Global Health Policy and 396 Health Systems Research Program, Netherlands, for providing funds for the conduct of the study (Grant number: 07.45.102.00) and Verena Schier for the use of her adapted Cochrane Bias Tools for assessing risk of bias.
None declared.