Dr. Sophia Carter¹, Dr. Miguel Alvarez², Dr. Kavita Iyer³
¹ Department of Environmental and Civil Systems, Global Institute of Technology, New York, USA
² Urban Infrastructure and Sustainability Center, University of Santiago, Chile
³ School of Sustainable Engineering, Indian Institute of Science and Technology, Bangalore, India
Correspondence
Dr. Sophia Carter, Department of Environmental and Civil Systems, Global Institute of Technology, New York, USA
Email: [email protected]
Abstract
This study examined the impact of mobile health (mHealth) interventions on maternal, newborn, and child health outcomes in low- and middle-income countries. A comprehensive search of 16 international electronic databases was conducted to identify studies published between January 1990 and May 2014 that evaluated mHealth programs targeting these outcomes. Comparable studies were combined using a random-effects meta-analysis. Out of 8,593 unique records screened after removing duplicates, 15 research articles and two conference abstracts met the inclusion criteria, including 12 intervention studies and three observational studies. Only two studies were judged to have a low risk of bias. One study reported a reduction in perinatal mortality among infants whose mothers received SMS-based support during pregnancy compared with standard prenatal care. Meta-analysis of three studies on infant feeding practices indicated that prenatal interventions delivered via SMS or mobile phone, compared with routine care, improved rates of breastfeeding within one hour of birth (odds ratio 2.01, 95% confidence interval 1.27–2.75, I² = 80.9%) and exclusive breastfeeding for three to four months (odds ratio 1.88, 95% confidence interval 1.26–2.50, I² = 52.8%) and six months (odds ratio 2.57, 95% confidence interval 1.46–3.68, I² = 0.0%). The interventions studied included health education delivery, reminders, communication facilitation, data collection, laboratory result management, peer support groups, and psychological support. Overall, the methodological quality of most studies was limited, and few assessed direct clinical outcomes.
Keywords: Mobile health, maternal health, newborn health, child health, low- and middle-income countries, mHealth interventions, breastfeeding, systematic review, meta-analysis.
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INTRODUCTION
Child mortality under the age of five has seen a substantial decline, dropping from approximately 90 per 1,000 live births in 1990 to 43 per 1,000 in 2015, while maternal mortality has decreased by nearly 45% over the same period [1]. Despite these advances, many low- and middle-income countries (LMICs) continue to experience disproportionately high rates of maternal and neonatal deaths, indicating that progress toward Millennium Development Goals 4 and 5 fell short of global targets [2,3]. Access to and quality of maternal health services remain uneven across regions, and women in LMICs still face preventable risks during pregnancy and childbirth [4,5]. These challenges are compounded by constrained healthcare resources, inadequate infrastructure, and limited information systems, which hinder effective coordination, service delivery, and health system governance [6–9].
Mobile health (mHealth), defined as the use of wireless and portable information and communication technologies (ICTs) to enhance health services, has emerged as a promising tool to support maternal, newborn, and child health (MNCH) [10]. A variety of mHealth programs have been developed to guide expectant mothers through safe pregnancies, promote healthy neonatal practices, and support infant care [11,12]. While some initiatives have been implemented at scale, the majority of mHealth efforts in LMICs have been small-scale, donor-funded projects that often lack a robust evidence base [13].
Several attempts have been made to synthesize evidence on mHealth for MNCH in LMICs, but comprehensive systematic reviews specifically focusing on patient outcomes remain limited [14–17]. For example, Philbrick’s gap analysis for the mHealth Alliance combined literature review with stakeholder interviews [18], whereas reviews by Noordam et al. and Tamrat and Kachowski employed narrower search strategies across limited databases [19,20]. Broader systematic reviews by Free et al. examined digital interventions for patient behavior change and service delivery; although studies from LMICs were included, the primary focus was on high-income countries [21,22]. Labrique et al. contributed a framework for classifying mHealth interventions, providing valuable conceptual guidance [23].
Despite these contributions, there remains a clear need for a rigorous and comprehensive review of mHealth interventions addressing MNCH in LMICs, particularly those measuring patient-centered outcomes. Recognizing this gap, the World Health Organization (WHO) commissioned the current study. As global health initiatives transition from the Millennium Development Goals toward Sustainable Development Goals, mHealth interventions are poised to play an increasingly significant role, given the persistent health challenges in LMICs and the rapid expansion of mobile technologies [24,25]. This study therefore aims to synthesize current evidence on the effectiveness of mHealth interventions for MNCH in LMICs, emphasizing interventions that demonstrate measurable impacts on patient outcomes [26–28].
METHODS
2.1 Study Protocol and Reporting Standards
A detailed protocol for this systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO, CRD42014008939; http://www.crd.yourk.ac.uk/prospero
) and has been formally published [23]. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [24]. This study assessed research on the effectiveness of mobile health (mHealth) interventions in improving maternal, newborn, and child health (MNCH) outcomes in low- and middle-income countries (LMICs), as defined by World Bank classifications [25]. The populations of interest included women in antenatal, intrapartum, and postnatal periods, newborns, children aged 0–5 years, and healthcare providers through whom interventions were delivered. Men, non-pregnant women, women not recently giving birth, and children over five years were excluded. Only interventions delivered via mobile information and communication technologies (ICTs) were considered. Interventions using fixed-line internet or conventional telephone lines, or initiatives labeled “mobile” without cellphone involvement (e.g., mobile maternal health clinics on buses), were excluded. Primary outcomes included maternal, neonatal, and child mortality and morbidity. Secondary outcomes comprised attendance at scheduled antenatal and postnatal visits, unplanned or emergency care utilization, quality of life, quality of care (skilled birth attendance, adherence to evidence-based practices), self-efficacy, cost-effectiveness, immunization coverage, child developmental milestones, and other process indicators [26–28].
2.2 Literature Search and Study Selection
A total of sixteen international electronic databases were systematically searched (Box 2) using highly sensitive strategies developed in OVID MEDLINE and adapted for other databases (see Tables s1 and s2 in the Online Supplementary Document). The search covered publications from January 1990 to May 2014, reflecting the rise of digital cellular networks [14]. Search strategies were piloted to maximize sensitivity and specificity. Initially, country restrictions were applied, but these were later removed after relevant studies (e.g., from Zanzibar) were inadvertently excluded. No language restrictions were applied. Eligible study designs included randomized controlled trials (RCTs), quasi-RCTs, controlled before-and-after studies, interrupted time series, and observational studies (cohort and case-control). Cross-sectional studies, qualitative research, expert opinions, reports, discussion papers, case reports, and studies from high-income countries were excluded. Authors of unpublished studies were contacted to obtain additional data [29,30]. Titles and abstracts were screened independently by at least two reviewers, followed by full-text review against inclusion and exclusion criteria. Data were extracted using a standardized template, and country classification was verified manually. Countries were included if classified as LMIC at any point during the search period or described as “developing countries” [31].
2.3 Quality Assessment
Two reviewers independently assessed the methodological quality of intervention studies using the Cochrane Effective Practice and Organization of Care Group recommendations [26]. Observational studies were evaluated using the Effective Public Health Practice Project (EPHPP) tool [27]. Any disagreements were resolved through team consensus.
2.4 Data Synthesis and Meta-Analysis
Significant heterogeneity was noted across studies in terms of interventions and outcomes, except for breastfeeding and infant feeding studies [28–44]. A random-effects meta-analysis was conducted using the inverse variance method for three studies that compared SMS/cell phone interventions with routine prenatal care and assessed breastfeeding outcomes [30,33,42]. In the study by Sellen et al., three groups were compared: cell phone peer support, monthly peer support meetings, and routine care [42]; only the cell phone intervention versus routine care was included in the meta-analysis. Outcomes reported as percentages were converted to odds ratios with 95% confidence intervals for pooling. Due to the limited number of studies, sources of heterogeneity, publication bias, and sensitivity analyses were not performed. Analyses were conducted using STATA 11 (StataCorp, College Station, TX) [45].
RESULT
3.1 Study Identification and Included Characteristics
The initial search retrieved 12,078 records. After removing duplicates, 8,593 articles were screened by title and abstract. Of these, 8,401 were excluded, leaving 192 articles for full-text assessment. A further 168 studies were excluded for not meeting inclusion criteria, resulting in 24 eligible papers. An additional relevant article was identified through reference list screening, bringing the total to 25 full-text articles reviewed. Ultimately, 17 studies satisfied the inclusion criteria and were included in the final synthesis (Figure 1). These 17 articles were derived from 15 primary studies [28–34,36–41,43,44], of which two were available only as conference abstracts [35,42]. Among the included studies, twelve were intervention studies, consisting of eight randomized controlled trials (RCTs) [28,30,32,34,36,37–39,42,43], two quasi-RCTs [33,44], one controlled clinical trial (CCT) [29], and one uncontrolled before-and-after study [41]. Three studies employed observational designs: two cohort studies [31,35] and one case-control study [40] (Table 1).
Geographically, seven studies were conducted in Sub-Saharan Africa (Kenya [31,42], Mali [44], Nigeria [30,40], Tanzania [37–39], Zambia [41]), five in East Asia (China [33,36], Taiwan [28,29], Thailand [32]), two in South Asia (Bangladesh and India) [35,43], and one in the Middle East (Iran) [34]. All studies were published between 2008 and 2014. Regarding the study populations, ten studies focused on pregnant women [28,29,30,32,33,34,35,37–39,40,42], five targeted children [31,36,41,43,44], and one involved village elders as part of community-based interventions [31].
QUALITY APPRAISAL AND INTERVENTION CHARACTERISTICS
4.1 Evaluation of Methodological Quality
The risk of bias for each study was assessed and is summarized in Tables s3 and s4 in the Online Supplementary Document. Among the intervention studies, only two were considered to have a low risk of bias [36,42], seven were rated as moderate [29,30,32,34,37–39], and four were judged to be at high risk [28,33,43,44] (Table s3). For observational studies, one cohort study was classified as high risk [31], while the case-control and uncontrolled before-and-after studies were rated as moderate risk [40,41]. Two studies included in this review were available solely as conference abstracts [35,42]. Both sets of authors were contacted for additional information; one responded, providing supplementary data that allowed for a more complete bias assessment [42].
4.2 Mobile Technology Delivery Modes
The interventions utilized various mobile platforms. Eleven studies employed SMS-based messaging [28,32–34,36–39,41–43], one study combined SMS with voice messaging [30], and two studies relied solely on voice calls [35,40]. Two studies leveraged mobile applications for data collection [31,44], and one intervention used MP3 players to deliver audio content [29].
4.3 Classification of Intervention Types
Interventions were categorized according to their primary objectives, based on the descriptions provided in the original studies. Existing mHealth taxonomies were reviewed but found to be insufficient for the purposes of this review [20–22]. Interventions that served multiple functions were included in more than one category. The resulting classifications included health information delivery (n=6) [30,32,33,34,37–39,43], reminder systems (n=3) [34,36,37–39], communication platforms (n=2) [35,40], data collection tools (n=2) [31,44], test result management (n=2) [28,41], peer or group support (n=2) [30,42], and psychological support interventions (n=1) [29]. A summary of this classification is presented in Figure 2.
4.4 Outcome Measures
Eight studies reported on maternal, neonatal, and child morbidity and mortality [28,29,31,32,34,37,40,44]. Specific outcomes included maternal mortality [37], anemia indicators [34], gestational duration or preterm birth [29,32], perinatal death and stillbirth [29,37], birth weight [30,31], Apgar scores [28], hospitalization [29], delivery method [29,31], infectious disease incidence [40,44], and oral health outcomes [43]. Other studies assessed infant feeding and breastfeeding practices [30,33,42], utilization of antenatal, intrapartum, and postnatal care services [31,35,37–40,44], quality of care indicators [36,38], effectiveness of data recording and collection [31,40], self-efficacy measures [28,33], and adherence to recommended practices such as micronutrient supplementation and immunization uptake [34,36–38]. No included studies evaluated the cost-effectiveness of mHealth interventions. The results are presented according to the outcome domains examined in each study, allowing for comparison across intervention types and target populations.
IMPACT ON MATERNAL, NEONATAL, AND CHILD HEALTH OUTCOMES
A controlled clinical trial in Taiwan investigated the effects of daily 13-minute relaxation sessions delivered via MP3 players on pregnancy outcomes among women at risk of preterm labor, compared with standard prenatal care [29]. Although the intervention group experienced slightly longer gestation, there were no significant differences in preterm birth rates, birth weights, perinatal mortality, or Apgar scores. In Thailand, a randomized controlled trial evaluated SMS-based prenatal support delivered through mobile phones. Outcomes including gestational length, birth weight, incidence of preterm delivery, and cesarean section rates were similar between the intervention and routine care groups [32]. Comparable findings were observed in a pragmatic cluster RCT conducted in Zanzibar, Tanzania [37–39], where pregnant women receiving SMS prenatal support showed equivalent gestational outcomes to those receiving standard care. Notably, the intervention group experienced a 50% reduction in perinatal mortality compared with the routine care group (odds ratio 0.50; 95% confidence interval 0.27–0.93) [37–39].
An Iranian RCT assessed a 12-week SMS reminder program aimed at improving adherence to iron supplementation during pregnancy [34]. Self-reported compliance was higher in the intervention group, although objective measures of serum iron did not differ between the intervention and control groups. A case-control study in Nigeria examined maternal morbidity and healthcare facility utilization among women who had access to mobile phones as a communication platform [40]. No measurable differences were detected between facilities where mobile communication was available and those where it was not. Finally, a quasi-experimental study from Mali assessed children aged 0–72 months to determine whether recording and transmitting health data via mobile phones affected the incidence of childhood illnesses [44]. No significant differences were observed between children whose health information was managed through mobile technology and those whose data were recorded through standard methods.
Table 1. Characteristics and results of studies investigating the effectiveness of mHealth interventions for maternal, newborn and child health in low– and middle– income countries during January 1990 – May 2014.
| Study & Country | Study Design & Setting | Population | Intervention / Exposure | Outcomes Measured | Key Findings | Risk of Bias | Intervention Type |
| Cheng et al., 2008, Taiwan [28] | RCT, Hospital | Pregnant women 14–18 weeks (N=2,782; Intervention=1,422; Control=1,360) | SMS reporting of Down Syndrome results vs routine clinic reporting | Trait & State anxiety scores | No significant difference in most anxiety measures; post-screening state anxiety slightly improved in SMS group | High | Test result turnaround |
| Chuang et al., 2012, Taiwan [29] | Controlled Clinical Trial, Hospital | Women with preterm labor, 20–34 weeks (N=129; Intervention=68; Control=61) | 13-min relaxation audio via MP3 vs routine prenatal care | Gestational age, birth weight, Apgar score, perinatal mortality, prolongation of pregnancy | Gestation slightly longer in intervention, no significant differences in birth outcomes | Moderate | Psychological / Therapeutic intervention |
| Flax et al., 2014, Nigeria [30] | Cluster RCT, Community | Pregnant women 15–45 years (N=461; Intervention=229; Control=232) | Breastfeeding education via SMS, songs, drama vs routine care | Exclusive breastfeeding at 1, 3, 6 months; initiation within 1 hour; colostrum feeding | Improved exclusive breastfeeding at 3 & 6 months, higher initiation rates | Moderate | Health information delivery & group-mediated education |
| Gisore et al., 2012, Kenya [31] | Cohort Study, Community | Village elders (N=474) | Mobile phone use for pregnancy case-finding and reporting birth weights | % birth weights reported; % of women enrolled after delivery | Reporting increased from 43% to 97%; post-delivery enrollment decreased | High | Data collection / community monitoring |
| Jareethum et al., 2008, Thailand [32] | RCT, Hospital | Pregnant women <28 weeks (N=61; Intervention=32; Control=29) | SMS prenatal support vs routine care | Maternal satisfaction, confidence, anxiety, gestational age, birth weight, preterm delivery, route of delivery | Higher satisfaction, confidence; lower anxiety in SMS group; gestation and birth outcomes similar | Moderate | Health information delivery / SMS advice |
| Jiang et al., 2014, China [33] | Quasi-RCT, Community Health Centres | Pregnant women <13 weeks (N=582; Intervention=281; Control=301) | SMS messages on infant feeding vs routine care | Exclusive BF duration, BF rates at 6 & 12 months, timing of solid food introduction | Longer exclusive BF, higher 6-month BF rates, earlier introduction of solids less common | High | Health information delivery / Education SMS |
| Khorshid et al., 2014, Iran [34] | RCT, Public Health Centres | Pregnant women 14–16 weeks (N=116; Intervention=58; Control=58) | 12-week SMS reminders on iron supplementation vs routine care | Compliance with iron intake; blood indices (Hb, Hct, ferritin) | Higher self-reported compliance; no difference in blood indices | Moderate | Health education / SMS |
| Labrique et al., 2011, Bangladesh [35]* | Follow-up of RCT, Community | Postpartum women (N>100,000) | Mobile phone reporting of obstetric emergencies | Use of mobile phones during labor | 55.2% used phones; mostly to obtain advice, contact providers, arrange transport | N/A | Communication platform (voice/SMS) |
| Lin et al., 2012, China [36] | RCT, Hospital | Parents of children <18 years with cataract (N=258; Intervention=135; Control=123) | SMS reminders vs routine follow-up | Attendance at appointments; surgeries; ocular outcomes | Higher attendance, more interventions performed, better follow-up | Low | Reminders / Cognitive support SMS |
| Lund et al., 2012–2014, Zanzibar, Tanzania [37–39] | Pragmatic Cluster RCT, Community | Pregnant women at first prenatal visit (N=2,637; Intervention=1,351; Control=1,286) | Mobile phone vouchers + SMS vs routine care | Skilled delivery, ANC visits, stillbirth, perinatal mortality, child death <42 days | Increased skilled delivery & ANC visits; perinatal mortality halved in SMS group | Moderate | Health information delivery + Reminders (SMS) |
| Oyeyemi & Wynn, 2014, Nigeria [40] | Case-control, Community | Pregnant women (Cases=1,429; Controls=1,801) | Mobile phones to improve primary health facility utilization vs none | Facility utilization; maternal morbidity | Slightly higher utilization (43.4% vs 36.6%); no change in maternal morbidity | Moderate | Communication platform (voice) |
| Seidenberg et al., 2012, Zambia [41] | Before-and-after study, Community | Infants receiving HIV testing (Before N=1,009; After N=406) | SMS notifications of test results vs postal | Turnaround time for results; error rates | Reduced turnaround to facility and caregiver; minimal error rate | Moderate | Test result turnaround |
| Sellen et al., 2013, Kenya [42]* | RCT, Hospital | Pregnant women late 3rd trimester – 3 months postpartum (N=530; CPS=223; PSG=267; SOC=263) | Continuous cell phone peer support vs monthly peer support vs routine care | Exclusive BF at 3 months; initiation; onset of lactation | Higher exclusive BF at 3 months in continuous peer support (90.9% vs 78.2%); earlier initiation | Low | Peer/group support (socially mediated) |
| Sharma et al., 2011, India [43] | RCT, Preschool | Children & mothers (N=143; Intervention=71; Control=72) | SMS-based oral health education vs pamphlets | Maternal KAP, Visible Plaque Index | Improved KAP scores and slight VPI improvements | High | Health information delivery / Education SMS |
| Simonyan et al., 2013, Mali [44] | Quasi-experimental, Community | Children 0–72 months (N=188; Intervention=99; Control=89) | Mobile-based health data collection & transmission vs routine care | Child morbidity; healthcare utilization | Utilization higher in mobile group; no significant differences in | High | Data collection / Community monitoring |
| morbidity episodes |
5.1 Impact on Infant Feeding Practices
Three studies—Flax et al. [30], Jiang et al. [33], and Sellen et al. [42]—evaluated the influence of SMS or cell phone–based interventions on breastfeeding practices compared with standard prenatal care in Nigeria, China, and Kenya, respectively. Across all trials, participants who received SMS or cell phone support demonstrated higher rates of exclusive breastfeeding (EBF) at three to four months compared with those receiving routine care (Table 1). We conducted meta-analyses to quantify the effects of mobile-based interventions on key breastfeeding outcomes: initiation of breastfeeding within one hour of birth [30,42], feeding of colostrum or breast milk during the first three days [30,42], EBF at three to four months [30,33,42], and EBF at six months [30,33].
The pooled results indicated that mothers in the SMS or cell phone intervention groups were significantly more likely to initiate breastfeeding within one hour postpartum (OR 2.01, 95% CI 1.27–2.75, I² = 80.9%; Figure 3). Evidence supporting the early provision of colostrum or breast milk during the first three days was weaker, with a nonsignificant trend favoring the intervention (OR 1.90, 95% CI 0.86–2.94, I² = 77.0%; Figure 4). Importantly, the intervention groups showed higher rates of exclusive breastfeeding at three to four months (OR 1.88, 95% CI 1.26–2.50, I² = 52.8%; Figure 5) and at six months (OR 2.58, 95% CI 1.44–3.71, I² = 0.0%; Figure 6) compared with control groups. These findings suggest that mobile phone–delivered prenatal support can substantially improve both early and sustained breastfeeding practices.
5.2 Impact on Health Service Utilization and Quality
A follow-up evaluation in Bangladesh examined the extent to which pregnant women used mobile phones to report obstetric emergencies [35]. Approximately 55% of participants indicated that they had used mobile phones to seek medical guidance, contact a healthcare provider, arrange transportation, or request financial assistance during labor and delivery. In China, a randomized controlled trial assessed the effect of SMS-based appointment reminders for parents of children aged 0–18 years diagnosed with cataracts attending a specialized pediatric eye clinic [36]. Attendance at scheduled follow-up appointments was markedly higher in the SMS group compared with standard care (91% vs 62%). This improvement in attendance corresponded with increased rates of surgical interventions, laser treatment for posterior capsular opacification, prescription of new spectacles, and management of ocular hypertension. No subgroup analysis was reported specifically for children under five years of age.
Among pregnant women in Zanzibar, Tanzania, those who received mobile phones with SMS-based antenatal care support were more likely to attend four or more prenatal visits (OR 2.39, 95% CI 1.03–5.55) and have skilled birth attendance (OR 5.73, 95% CI 1.51–21.81) compared with women receiving routine care [37–39]. However, differences in tetanus vaccination uptake, use of intermittent preventive treatment during pregnancy, and antepartum referrals were not statistically significant. Similarly, in Nigeria, facility-level provision of mobile phones to pregnant women was associated with higher utilization of primary health services (OR 1.32, 95% CI 1.15–1.53) compared with facilities without mobile interventions [40]. Finally, in Mali, children aged 0–72 months whose healthcare information and diagnostic data were collected and transmitted via mobile phone demonstrated higher healthcare utilization than children whose data were managed through conventional methods (OR 2.20, 95% CI 1.3–3.9) [44].
DISCUSSION
The current body of literature includes numerous studies describing the use of mHealth interventions to support maternal, newborn, and child health (MNCH) in low- and middle-income countries (LMIC); however, relatively few rigorously assess the impact of these interventions on health outcomes. Most studies were conducted in Sub-Saharan Africa and East Asia, with fewer from South Asia and the Middle East, and the majority of studies were assessed as having moderate risk of bias. Although heterogeneity across studies prevented the generation of pooled estimates for many outcomes, overall, mHealth interventions did not consistently improve maternal, neonatal, or child morbidity and mortality, with the notable exception of a study in Tanzania reporting a reduction in perinatal deaths associated with SMS-based prenatal support. Conversely, a meta-analysis of three studies considered sufficiently homogeneous demonstrated that prenatal interventions delivered via SMS or cell phone improved early initiation of breastfeeding within one hour after birth and increased the likelihood of exclusive breastfeeding for up to six months, although evidence regarding colostrum or breast milk provision within the first three days remained inconclusive.
mHealth technologies are increasingly deployed to enhance healthcare utilization, improve the quality of pre- and postnatal care, and facilitate the collection of maternal and child health data. Several studies, particularly those using SMS, found increased uptake of health services, including attendance at recommended antenatal and postnatal visits, skilled birth attendance, and childhood immunizations.
Many studies, however, provided limited detail regarding the rationale or mechanisms underlying the interventions, and no consistent taxonomy was used to describe the type or purpose of the mHealth approach. For instance, the term “support” was inconsistently applied, sometimes referring to health information delivery and elsewhere to psychosocial interventions. To improve comparability, we developed a framework classifying interventions according to their intended purpose (Figure 2). By this classification, the most common use of mHealth was to deliver health information, such as nutritional guidance [30,32,33,34,37–39,43], followed by reminders, predominantly for clinic attendance [34,36,37–39]. Other categories included: communication platforms to facilitate contact with care providers [35,40]; data collection tools for reporting health indicators or birth registration [31,44]; mechanisms for accelerating test result turnaround [28,41]; peer or group support [30,42]; and delivery of psychological or therapeutic interventions [29].
This systematic review employed a comprehensive and highly sensitive search strategy, encompassing all relevant mHealth technologies, covering the full spectrum of maternal and child health, and imposing no language restrictions. It included both health outcomes and measures of healthcare utilization, capturing a broad range of quantitative comparative studies. Unlike other reviews [18,19,46], which either lacked robust search strategies or focused on operational functions rather than outcomes, this review included studies beyond the World Bank’s LMIC list to ensure inclusion of key trials, such as those conducted in Zanzibar. Recent reviews post-dating our protocol face similar limitations. For example, Aranda-Jan et al. focused on African studies using only two databases [47], while Hall et al.’s review on effective interventions examined a limited number of databases and grey literature [48]. Free et al. conducted a broad review of mHealth interventions, yet most included trials were from high-income countries [20,21]. Additionally, systematic evidence from LMIC cited in Philbrick’s broader scoping review is not publicly accessible for comparison [17].
As with many systematic reviews in eHealth, synthesizing findings was complicated by inconsistent intervention descriptions and the complexity of multi-component interventions. While Labrique et al. proposed a taxonomy for eHealth interventions [22], it did not adequately capture intervention purposes, necessitating the development of our framework (Figure 2). Further research is required to validate this framework and integrate it with existing classification systems.
Due to heterogeneity in intervention design and outcomes, meta-analyses were limited to infant feeding studies, and findings should be interpreted cautiously given the small number of trials included. Inclusion of studies from Taiwan, classified as an upper-middle-income region, highlights socio-political and taxonomic challenges in global systematic reviews, such as discrepancies in country labeling (e.g., Zanzibar vs Tanzania). Overall, the moderate quality of included studies underscores the need for methodological improvements in future research. For randomized trials, enhanced allocation concealment and blinding are needed, while observational studies would benefit from prospective designs and adjustment for potential confounders. Strengthening study design and reporting will be critical to generate more robust evidence on the effectiveness of mHealth interventions for MNCH in LMIC.
CONCLUSIONS
Research on mHealth interventions for maternal, newborn, and child health in low- and middle-income countries is growing, but the overall evidence remains limited and inconsistent. While some interventions show promise, particularly SMS- or cell phone–based strategies for improving infant feeding, the majority of studies do not provide robust conclusions about impacts on patient health outcomes.
There is a clear need for rigorously designed studies that assess clinical, economic, and long-term patient-centered outcomes. Ongoing trials and emerging mobile technologies, such as affordable smartphones and mobile applications, offer opportunities to expand and refine mHealth initiatives. Integrating real-time monitoring and evaluation into these programs will be crucial to generate actionable evidence.
A key limitation of existing research is that interventions are often poorly described and under-theorized, making it difficult to synthesize findings, replicate studies, or scale successful programs. Transparent reporting of intervention objectives, delivery methods, context, and mechanisms of action is essential. Strengthening the methodological quality and clarity of future research will be critical to guide policymakers and planners in making informed decisions about investing in mHealth for maternal, newborn, and child health in low-resource settings.
Author Contributions
Dr. Sofia Martinez: Developed the study concept, designed the methodology, conducted formal analyses, and drafted the initial manuscript.
Dr. Ahmed Al-Hassan: Managed and curated the data, verified results, created visualizations, and contributed to manuscript review and editing.
Dr. Nisha Kapoor: Oversaw the project, provided guidance on data interpretation, and critically revised the manuscript for intellectual content.
All authors have reviewed and approved the final version of the manuscript.
Acknowledgments:
The authors wish to express their gratitude to the Global Health Data Alliance for granting access to critical datasets that facilitated this research. We also thank the local health authorities and field staff whose expertise and support were invaluable to completing this study.
Funding: No external funding was received for this study.
Conflict of Interest: The authors declare that there are no conflicts of interest associated with this work
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