Systematic review on the cost and cost-effectiveness of mHealth interventions supporting women during pregnancy



      The increased integration of digital health into maternity care—alongside growing use of, and access to, personal digital technology among pregnant women—warrants an investigation of the cost-effectiveness of mHealth interventions used by women during pregnancy and the methodological quality of the cost-effectiveness studies.


      A systematic search was conducted to identify peer-reviewed studies published in the last ten years (2011–2021) reporting on the costs or cost-effectiveness of mHealth interventions used by women during pregnancy. Available data related to program costs, total incremental costs and incremental cost-effectiveness ratios (ICERs) were reported in 2020 United States Dollars. The quality of cost-effectiveness studies was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS).


      Nine articles reporting on eight studies met the inclusion criteria. Direct intervention costs ranged from $7.04 to $86 per woman, total program costs ranged from $241,341 to $331,136 and total incremental costs ranged from -$21.16 to $1.12 million per woman. The following ICERs were reported: $2168 per DALY averted, $203.44 per woman ceasing smoking, and $3475 per QALY gained. The full economic evaluation studies (n = 4) were moderate to high in quality and all reported the mHealth intervention as cost-effective. Other studies (n = 4) were low to moderate in quality and reported low costs or cost savings associated with the implementation of the mHealth intervention.

      Conclusions for practice

      Preliminary evidence suggests mHealth interventions may be cost-effective and “low-cost” but more evidence is needed to ascertain the cost-effectiveness of mHealth interventions regarding positive maternal and child health outcomes and longer-term health service utilisation.


      ICER (Incremental Cost-Effectiveness Ratio), CHEERS (Consolidated Health Economic Evaluation Reporting Standards), DALY (Disability Adjusted Life Year), QALY (Quality Adjusted Life Year), SMS (Short Message Service), ICT (Information and Communication Technology), NHS (National Health Service), PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses), USD (United States Dollar), LMIC (Low-to-Middle-Income Country)


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        • van den Heuvel J.F.
        • Groenhof T.K.
        • Veerbeek J.H.
        • et al.
        eHealth as the next-generation perinatal care: an overview of the literature.
        J. Med. Internet Res. 2018; 20e202
      1. World Health Organization. WHO guideline: recommendations on digital interventions for health system strengthening. Geneva: World Health Organization; 2019.

        • Musiimenta A.
        • Tumuhimbise W.
        • Mugyenyi G.
        • et al.
        Mobile phone-based multimedia application could improve maternal health in rural southwestern uganda: mixed methods study.
        Online J. Public Health Inform. 2020; 12e8
        • Nyati-Jokomo Z.
        • Dabengwa I.M.
        • Makacha L.
        • et al.
        RoadMApp: a feasibility study for a smart travel application to improve maternal health delivery in a low resource setting in Zimbabwe.
        BMC Pregnancy Childbirth. 2020; 20
        • Tumuhimbise W.
        • Atukunda E.C.
        • Ayebaza S.
        • et al.
        Maternal health-related barriers and the potentials of mobile health technologies: Qualitative findings from a pilot randomized controlled trial in rural Southwestern Uganda.
        J. Family Med. Prim. Care. 2020; 9: 3657-3662
        • Hussain T.
        • Smith P.
        • Yee L.M.
        Mobile phone-based behavioral interventions in pregnancy to promote maternal and fetal health in high-income countries: systematic review.
        JMIR mHealth uHealth. 2020 28; 8e15111
        • Sondaal S.F.
        • Browne J.L.
        • 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; 11e0154664
        • Marko K.I.
        • Ganju N.
        • Krapf J.M.
        • et al.
        A mobile prenatal care app to reduce in-person visits: prospective controlled trial.
        JMIR mHealth uHealth. 2019; 7e10520
        • Su Y.
        • Heitner J.
        • Yuan C.
        • et al.
        Effect of a text messaging-based educational intervention on cesarean section rates among pregnant women in china: quasirandomized controlled trial.
        JMIR mHealth uHealth. 2020; 8e19953
        • Kern-Goldberger A.R.
        • Srinivas S.K.
        Telemedicine in obstetrics.
        Clin. Perinatol. 2020; 47: 743-757
        • Palmer K.R.
        • Tanner M.
        • Davies-Tuck M.
        • et al.
        Widespread implementation of a low-cost telehealth service in the delivery of antenatal care during the COVID-19 pandemic: an interrupted time-series analysis.
        Lancet. 2021; 398: 41-52
        • Ramakrishnan R.
        • Rao S.
        • He J.-R.
        Perinatal health predictors using artificial intelligence: a review.
        Women’s Health. 2021;
        • Hanach N.
        • de Vries N.
        • Radwan H.
        • et al.
        The effectiveness of telemedicine interventions, delivered exclusively during the postnatal period, on postpartum depression in mothers without history or existing mental disorders: a systematic review and meta-analysis.
        Midwifery. 2021; 94102906
        • Ming W.K.
        • Mackillop L.H.
        • Farmer A.J.
        • et al.
        Telemedicine technologies for diabetes in pregnancy: a systematic review and meta-analysis.
        J. Med. Internet Res. 2016; 18e290
        • Overdijkink S.B.
        • Velu A.V.
        • Rosman A.N.
        • et al.
        The usability and effectiveness of mobile health technology-based lifestyle and medical intervention apps supporting health care during pregnancy: systematic review.
        JMIR mHealth uHealth. 2018; 6e109
        • Rasekaba T.M.
        • Furler J.
        • Blackberry I.
        • et al.
        Telemedicine interventions for gestational diabetes mellitus: a systematic review and meta-analysis.
        Diabetes Res. Clin. Pract. 2015; 110: 1-9
      2. Centre for Reviews and Dissemination. Search strategies. University of York: Centre for Reviews and Dissemination; 2014; 〈〉.

        • Eddy K.S.N.
        • Homer C.
        • McDonald S.
        • et al.
        Cost-effectiveness evidence for maternal and perinatal health interventions: protocol for living scoping review.
        Center Open Sci. 2020; ([])
        • Page M.J.
        • McKenzie J.E.
        • Bossuyt P.M.
        • et al.
        The PRISMA 2020 statement: an updated guideline for reporting systematic reviews.
        J. Clin. Epidemiol. 2021; 134: 178-189
      3. National Institutes of Health. Module 3: Identification and Retrieval of Published Health Economic Evaluation Studies. In: Health Economics Information Resources: A Self-Study Course. National Institutes of Health, 2016; [〈〉].

      4. Veritas Health Innovation. Covidence systematic review software.

      5. Aluko P., Graybill E., Craig D., et al. Chapter 20: Economic Evidence. In: Higgins J, Thomas J, Chandler M, et al., eds. Cochrane Handbook for Systematic Reviews of Interventions. Vol. 6.2. Cochrane; 2021: [〈〉].

        • Lawrie T.A.
        • Rogozińska E.
        • Sobiesuo P.
        • et al.
        A systematic review of the cost-effectiveness of uterotonic agents for the prevention of postpartum hemorrhage.
        Int. J. Gynaecol. Obstet. 2019; 146: 56-64
      6. Craig D., Rice S. CRD Report 6: NHS Economic Evaluation Database Handbook, 3rd ed. Centre for Reviews and Dissemination, University of New York; 2007.

      7. OECD ; Exchange rates (indicator); (Accessed 29 November 2021); doi: 〈10.1787/037ed317-en〉.

        • Husereau D.
        • Drummond M.
        • Petrou S.
        • et al.
        Consolidated health economic evaluation reporting standards (CHEERS) statement.
        Value Health. 2013; 16: e1-e5
        • Naughton F.
        • Cooper S.
        • Foster K.
        • et al.
        Large multi-centre pilot randomized controlled trial testing a low-cost, tailored, self-help smoking cessation text message intervention for pregnant smokers (MiQuit).
        Addiction. 2017; 112: 1238-1249
        • Jones M.
        • Smith M.
        • Lewis S.
        • et al.
        A dynamic, modifiable model for estimating cost-effectiveness of smoking cessation interventions in pregnancy: application to an RCT of self-help delivered by text message.
        Addiction. 2019; 114: 353-365
        • Redman L.M.
        • Gilmore L.A.
        • Breaux J.
        • et al.
        Effectiveness of smartmoms, a novel ehealth intervention for management of gestational weight gain: randomized controlled pilot trial.
        JMIR mHealth uHealth. 2017; 5e133
        • Jo Y.
        • LeFevre A.E.
        • Healy K.
        • et al.
        Costs and cost-effectiveness analyses of mCARE strategies for promoting care seeking of maternal and newborn health services in rural Bangladesh.
        PLoS One. 2019; 14e0223004
        • LeFevre A.
        • Cabrera-Escobar M.A.
        • Mohan D.
        • et al.
        Forecasting the value for money of mobile maternal health information messages on improving utilization of maternal and child health services in gauteng, south africa: cost-effectiveness analysis.
        JMIR mHealth uHealth. 2018; 6e153
        • O’Sullivan E.J.
        • Rokicki S.
        • Kennelly M.
        • et al.
        Cost-effectiveness of a mobile health-supported lifestyle intervention for pregnant women with an elevated body mass index.
        Int. J. Obes. 2020; 44: 999-1010
        • Alhaidari T.
        • Amso N.
        • Jawad T.M.
        • et al.
        Feasibility and acceptability of text messaging to support antenatal healthcare in Iraqi pregnant women: a pilot study.
        J. Perinat. Med. 2018; 46: 67-74
        • Krishnamurti T.
        • Davis A.L.
        • Wong-Parodi G.
        • et al.
        Development and testing of the myhealthypregnancy app: a behavioral decision research-based tool for assessing and communicating pregnancy risk.
        JMIR mHealth uHealth. 2017; 5e42
        • Mackillop L.
        • Hirst J.E.
        • Bartlett K.J.
        • et al.
        Comparing the efficacy of a mobile phone-based blood glucose management system with standard clinic care in women with gestational diabetes: randomized controlled trial.
        JMIR mHealth uHealth. 2018; 6e71
        • Barbosa W.
        • Zhou K.
        • Waddell E.
        • Myers T.
        • Dorsey E.R.
        Improving access to care: telemedicine across medical domains.
        Annu. Rev. Public Health. 2021; 42: 463-481
        • Mackert M.
        • Mandell D.
        • Donovan E.
        • Walker L.
        • Henson-García M.
        • Bouchacourt L.
        Mobile apps as audience-centered health communication platforms.
        JMIR mHealth uHealth. 2021; 9e25425
        • Lee S.H.
        • Nurmatov U.B.
        • Nwaru B.I.
        • Mukherjee M.
        • Grant L.
        • Pagliari C.
        Effectiveness of mHealth interventions for maternal, newborn and child health in low- and middle-income countries: Systematic review and meta-analysis.
        J. Glob. Health. 2016; 6010401
        • Lee Y.
        • Moon M.
        Utilization and content evaluation of mobile applications for pregnancy, birth, and child care.
        Healthc. Inform. Res. 2016; 22: 73-80
        • Smith B.
        • Magnani J.W.
        New technologies, new disparities: the intersection of electronic health and digital health literacy.
        Int. J. Cardiol. 2019; 292: 280-282