Published on in Vol 20, No 6 (2018): June

Preprints (earlier versions) of this paper are available at, first published .
eHealth as the Next-Generation Perinatal Care: An Overview of the Literature

eHealth as the Next-Generation Perinatal Care: An Overview of the Literature

eHealth as the Next-Generation Perinatal Care: An Overview of the Literature


1Division of Woman and Baby, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands

2Department of Clinical Chemistry and Hematology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands

*these authors contributed equally

Corresponding Author:

Josephus FM van den Heuvel, MSc, MD

Division of Woman and Baby

University Medical Center Utrecht

Utrecht University

Lundlaan 6

Utrecht, 3584 AB


Phone: 31 887554913

Fax:31 887555320


Background: Unrestricted by time and place, electronic health (eHealth) provides solutions for patient empowerment and value-based health care. Women in the reproductive age are particularly frequent users of internet, social media, and smartphone apps. Therefore, the pregnant patient seems to be a prime candidate for eHealth-supported health care with telemedicine for fetal and maternal conditions.

Objective: This study aims to review the current literature on eHealth developments in pregnancy to assess this new generation of perinatal care.

Methods: We conducted a systematic literature search of studies on eHealth technology in perinatal care in PubMed and EMBASE in June 2017. Studies reporting the use of eHealth during prenatal, perinatal, and postnatal care were included. Given the heterogeneity in study methods, used technologies, and outcome measurements, results were analyzed and presented in a narrative overview of the literature.

Results: The literature search provided 71 studies of interest. These studies were categorized in 6 domains: information and eHealth use, lifestyle (gestational weight gain, exercise, and smoking cessation), gestational diabetes, mental health, low- and middle-income countries, and telemonitoring and teleconsulting. Most studies in gestational diabetes and mental health show that eHealth applications are good alternatives to standard practice. Examples are interactive blood glucose management with remote care using smartphones, telephone screening for postnatal depression, and Web-based cognitive behavioral therapy. Apps and exercise programs show a direction toward less gestational weight gain, increase in step count, and increase in smoking abstinence. Multiple studies describe novel systems to enable home fetal monitoring with cardiotocography and uterine activity. However, only few studies assess outcomes in terms of fetal monitoring safety and efficacy in high-risk pregnancy. Patients and clinicians report good overall satisfaction with new strategies that enable the shift from hospital-centered to patient-centered care.

Conclusions: This review showed that eHealth interventions have a very broad, multilevel field of application focused on perinatal care in all its aspects. Most of the reviewed 71 articles were published after 2013, suggesting this novel type of care is an important topic of clinical and scientific relevance. Despite the promising preliminary results as presented, we accentuate the need for evidence for health outcomes, patient satisfaction, and the impact on costs of the possibilities of eHealth interventions in perinatal care. In general, the combination of increased patient empowerment and home pregnancy care could lead to more satisfaction and efficiency. Despite the challenges of privacy, liability, and costs, eHealth is very likely to disperse globally in the next decade, and it has the potential to deliver a revolution in perinatal care.

J Med Internet Res 2018;20(6):e202



Electronic Health—A New Opportunity?

Health care is facing the emergence of a new range of systems, services, and applications using electronic communication. Electronic health (eHealth) is the network of technology applications regarding health issues, including, for example, Web-based informative programs, remote monitoring, teleconsultation, and mobile device–supported care [1]. As the health care costs in developed countries continue to increase, policies for cost reduction without concessions to the quality of care are being imposed. Unrestricted by time and place, eHealth applications also provide solutions for patient empowerment and value-based health care [2]. Patient empowerment is assumed to improve patient participation in medical decision making, commitment to treatment, and thus, health outcomes [3-5]. The boost in patient engagement can be an important factor for the improvement of quality of care and patient safety [6].

Young women in their reproductive years are frequent users of internet, social media, and smartphone apps [7]. The internet is ever more utilized for the search of health information on prenatal, perinatal, and postnatal topics [8]. Furthermore, the Web is also used as a forum for the exchange of experiences and peer support [9]. Figure 1 shows multiple domains of perinatal care in which eHealth is already being used by patients and health care providers.

Protocols of professionals’ associations and institutions contain little communication regarding eHealth. No statements are made regarding eHealth in guidelines from the British Royal College of Obstetricians and Gynaecologists, the National Institute for Health and Care Excellence, and the American College of Obstetrics and Gynecology. The Dutch Association of Obstetrics and Gynecology notes that developments in eHealth should be actively implemented in obstetric healthcare to induce the shift of scheduled care to the home setting and thus lower the in-hospital care burden [10].

Figure 1. Electronic health (eHealth) solutions in 6 domains of perinatal care.
View this figure


eHealth has the potential to fulfill a key role in the transformation of the health care system for both patients and caregivers. However, questions are raised if eHealth can deliver the quality of care that is required to remain or even improve health outcomes. It is evident that there is a need for guidance and management of quality standards. Issues of costs and reimbursement; safety of data collection; and storage, privacy, and reliability of information on websites and in apps should also be taken into account.

Our aim is to provide a comprehensive and contemporary overview of the literature on eHealth in perinatal care and assess the applicability, advantages, limitations, and future of this new generation of pregnancy care.

A systematic literature search was performed in PubMed and EMBASE in June 2017, combining various synonyms for perinatal care and telemedicine and eHealth (see Multimedia Appendix 1 for the search strategy). Studies reporting the use of eHealth during prenatal, perinatal, and postnatal care were included. Due to the rapid developments in this field and our contemporary scope, we excluded articles describing outdated technologies, for example, fax communication, phonocardiography, and home visits or home care. Screening and reviewing the abstracts and full articles was done by 2 independent authors (JH and KG). Given the heterogeneity in study methods, used technologies, and outcome measurements, results were analyzed and presented in a narrative overview of the literature.

Study Selection

Literature search and reference screening provided 71 studies of interest (see Multimedia Appendix 1 for the flow diagram of selection of studies). All articles were categorized in 6 domains, which will be addressed accordingly: information and eHealth use, lifestyle (gestational weight gain, exercise, and smoking cessation), gestational diabetes, mental health, low- and middle-income countries, and telemonitoring/teleconsulting (see also Figure 1). Tables 1-3 show the overview of 71 publications in 6 domains of perinatal care in which eHealth use in patient care was described, implemented, or compared with standard care.

Information and eHealth Use in Pregnancy

In 15 studies, the characteristics of eHealth users in the perinatal period were described (Table 1). Around 88% (31/35) of participants owned a smartphone [11]. Usage of websites and pregnancy apps for medical information varies from 50% to 98% [7,11-14]. Online information-seeking behavior is common in pregnant women in general and it is not restricted to women with a special profile based on age, education, or social support [7]. Increased knowledge on pregnancy complications has also shown to reduce maternal anxiety and costly hospital visits [15,16]. Factors associated with app use in pregnancy are younger age, nulliparity, lower self-rated health, and higher education. Furthermore, 25.6% (56/219) of questioned women showed interest in a tailored pregnancy app initiated by their health care provider [7,14].

The most searched topics are fetal development, pregnancy complications, healthy lifestyle during pregnancy, generic and specific guidance/advices during pregnancy, and lactation [13,17]. Although they value the Web-based medical information as moderately reliable, 71.3%-75.1% (582/800) of the women do not discuss the information found on internet with their gynecologist [17,18]. One study reported that their lifestyle app helped women to initiate the conversation with their health caregiver on this subject [19].

There is an increasing use of internet for health information, including the perinatal period. However, websites are often contradictory and this may lead to confusion [20]. eHealth may be helpful to address questions through informative websites, apps, and peer support platforms designed by health professionals. Furthermore, eHealth may provide possibilities for decision support in more complicated pregnancies [21].

Health Outcome After eHealth Intervention

The effect on health is the most important issue to address in the effective implementation of eHealth in perinatal care. Parameters for quality standards include disease outcomes, enhancing patient adherence to treatment, reducing overuse, and increasing access to care [29]. Results of the search showed that most publications focus on the improvement of lifestyle (gestational weight gain, exercise, smoking cessation), gestational diabetes monitoring, mental health, care in lower- and middle-income countries, and telemonitoring.


Our search provided 13 publications describing health outcomes for eHealth interventions on lifestyle during pregnancy (Table 2). Pursuing a healthy lifestyle has proven to be beneficial for pregnancy outcomes such as preterm birth, gestational diabetes, or pre-eclampsia [30-32]. Participant motivation, reducing the dropout rate, and sustainability of long-term results are notoriously difficult in lifestyle studies. Smartphone technologies provide features to overcome these obstacles. Results from feasibility studies show good acceptability, adherence, and engagement for eHealth interventions for healthy gestational weight gain and physical activity, favoring an app over a website [33,34]. Physical activity trials with tailored text messaging (short message service, SMS) services resulted in an increase in step count up to 4 times more than in the control group. In addition, eHealth interventions resulted in better perceived health in pregnancy and lower, healthier gestational weight gain in both nonobese (7.8 kg vs 9.7 kg) and obese women (6.65 kg vs 9.74 kg) [35-37]. Dietary apps directed at healthy gestational weight gain are still in developmental and experimental phase [27,38,39].

Smoking during pregnancy increases the risk of unfavorable pregnancy outcomes. In 2010, approximately 10% of the women smoked cigarettes during pregnancy, especially younger, non-white mothers of a lower social economic status [40,41]. The 2016 review by Heminger et al summarizes the studies performed on SMS programs and mobile apps for smoking cessation in pregnancy [42]. Women participating in SMS cessation programs report relatively high abstinence of 38% in the first week and 54% in the second week (n=20). Biochemically confirmed abstinence rates were 12.5% in participants compared with 7.8% in controls (n=207). Smartphone apps were preferred over SMS-driven programs, as seen in over 10,000 installations of apps compared with 20-800 registrations in SMS programs.

Gestational Diabetes

About 5% to 7% of all pregnancies are complicated by gestational diabetes mellitus (GDM) in the United Kingdom and United States (range 1%-25%) [43]. Pregnancies with GDM are associated with perinatal complications such as caesarean section, shoulder dystocia, and neonatal hypoglycemia. Extensive glucose monitoring during pregnancy is a burden for both patients and health care budgets. eHealth in GDM care has evolved most notably of all perinatal appliances of eHealth the last 3 years [44]. We found 13 studies on this topic, including 2 systematic reviews (Table 2). Developments involve smartphone-facilitated remote blood glucose monitoring, management of medication schedules through Web-based or SMS-facilitated feedback systems, and telephone review service to support and supervise glycemic control [45-51]. Overall, studies showed a decrease in planned and unplanned visits by 50% to 66%, whereas no unfavorable differences in glycemic control, maternal, and neonatal outcomes occurred [47-49,52]. Two recent systematic reviews with meta-analysis confirm these results [53,54]. No cost-effectiveness analysis was performed due to insufficient data. There is also increasing evidence of GDM as a risk factor for type 2 diabetes later in life [55]. eHealth programs for follow-up of women with a history of GDM are being developed but need to be examined more thoroughly [45].

Table 1. Information and electronic health (eHealth) use in pregnancy: overview of the literature.
ReferenceMethodsNTechnology/eHealth intervention
Sayakhot et al [12]Systematic review (with 7 cross-sectional studies)3359Patients’ use of internet for pregnancy information
Ledford et al [22]RCTa pilot150App for pregnancy education and record keeping
Walker et al [15]Prospective cohort8Website for education on placental complications
Bush et al [23]Before-after study85Prenatal care app use and user engagement
Wallwiener et al [7]Cross sectional220Surveys and questionnaires on use of eHealth (smartphones, internet, apps) during pregnancy
Scaioli et al [13]Cross sectional1347Surveys and questionnaires on use of eHealth (smartphones, internet, apps) during pregnancy
Peragallo et al [24]Cross sectional100Surveys and questionnaires on use of eHealth (smartphones, internet, apps) during pregnancy
Lee et al [14]Cross sectional193Surveys and questionnaires on use of eHealth (smartphones, internet, apps) during pregnancy
Lupton et al [25]Cross sectional410Surveys and questionnaires on use of eHealth (smartphones, internet, apps) during pregnancy
Narasimhulu et al [17]Cross sectional586Surveys and questionnaires on use of eHealth (smartphones, internet, apps) during pregnancy
Goetz et al [26]Qualitative research30Focus groups and interviews on eHealth use and implementation (in pregnant women, men, and clinicians)
Willcox et al [27]Qualitative research27Focus groups and interviews on eHealth use and implementation (in pregnant women, men, and clinicians)
Rodger et al [11]Qualitative research35Focus groups and interviews on eHealth use and implementation (in pregnant women, men, and clinicians)
Mackert et al [28]Qualitative research32Focus groups and interviews on eHealth use and implementation (in pregnant women, men, and clinicians)
Lupton et al [25]Qualitative research36Focus groups and interviews on eHealth use and implementation (in pregnant women, men, and clinicians)

aRCT: randomized controlled trial.

Table 2. Health outcome of electronic health (eHealth) use in lifestyle and gestational diabetes mellitus management in pregnancy: overview of the literature.
Study domain and referenceMethodsNTechnology/eHealth intervention
Lifestyle: Gestational weight gain, exercise, smoking cessation (13 studies)

O’Brien et al [79]Systematic review (with 7 studies)33Technology-supported diet and lifestyle interventions

Pollak et al [80]RCTa33SMSb programs on healthy lifestyle

Soltani et al [35]RCT14SMS for heathy lifestyle in women with BMIc >30

Graham et al [81]RCT1335Internet-based platform to prevent excessive weight gain

Hayman et al [34]RCT77Web-based physical activity intervention

Huberty et al [82]RCT80SMS programs to increase physical activity

Willcox et al [37]RCT91Healthy gestational weight gain for obese pregnancies

Knight et al [19]One group pilot10App with information for lifestyle behavior

Waring et al [33]Cross sectional64Survey on interest in lifestyle app or website

Choi et al [36]RCT pilot30Activity app+pedometer wearable

Lewis et al [83]Observational cohort37Exercise with SMS or app-based support

Guo et al [84]One group pilot50Video program with yoga via Facebook or DVD

Heminger et al [42]Systematic review (with 7 RCTs)702SMS or app support on smoking: quitting date, relapse, information, daily messages
Gestational diabetes mellitus (13 studies)

Ming et al [54]Systematic review (with 7 RCTs)579Telemedicine for diabetes in pregnancy

Rasekaba et al [53]Systematic review (with 3 RCTs)243Telemedicine for glucose monitoring

Kruger et al [85]RCT18Telemedicine for glucose monitoring

Dalfra et al [86]RCT276Telemedicine for glucose monitoring

Perez-Ferre et al [52]RCT100Telemedicine for glucose monitoring

Wojcicki et al [87]RCT30Telemedicine for glucose monitoring

Carral et al [49]Prospective cohort104Web-based telemedicine system

Given et al [50]Feasibility study50Web-based telemedicine system

Nicholson et al [88]Feasibility study23Web-based self monitoring, diary

Mackillop et al [51]Pilot study48Smartphone app with blood glucose meter

Ganapathy et al [89]Pilot study50Remote blood pressure measurements

Khorshidi et al [45]RCT80Postpartum screening after GDMd

Harrison et al [90]Survey+interviews70Acceptability of telemedicine for GDM patients

aRCT: randomized controlled trial.

bSMS: short message services.

cBMI: body mass index.

dGDM: gestational diabetes mellitus.

Table 3. Health outcome of electronic health (eHealth) use in electronic mental (e-mental) health, low- and middle-income countries, and telemonitoring and teleconsultation in pregnancy: overview of the literature.
Study domain and referenceMethodsNTechnology/eHealth intervention
E-mental health (16 studies)

Lau et al [64]Systematic review (with 8 RCTsa)1523Therapist-supported internet-based cognitive behavior therapy among postpartum women

Lee et al [61]Systematic review (with 4 RCTs)1274Cognitive behavioral therapy with internet

Ashford et al [63]Systematic review (with 11 studies)1537Web-based perinatal mental health interventions

Milgrom et al [91]RCT43Cognitive behavioral therapy with internet

Ngai et al [92]RCT397Telephone-based cognitive-behavioral Therapy

Shamshiri Milani et al [93]RCT54Telephone-based cognitive-behavioral therapy

Kingston et al [60]RCT636Acceptability of e-screening for mental health

Fontein et al [94]Before-after study433Website for maternal stress prevention

Jimenez-Serrano et al [59]Prospective cohort1880App screening for postpartum depression

Posmontier et al [62]Prospective cohort61Telephone-administered psychotherapy

Letourneau et al [65]Prospective cohort64Telephone-based peer support intervention

Broom et al [95]Observational54Supportive text messaging in postpartum depression

Mitchell et al [58]Cross sectional106Telephone screening for postpartum depression

Figueiredo et al [96]Cross sectional90Telephone screening for postpartum depression

Pugh et al [97]Case study1Therapeutic assistance with email and SMSb

Pineros-Leano et al [98]Qualitative25Screening for postpartum depression using mobile health
Low and middle income countries (2 studies)

Lee et al [67]; Sondaal et al [66]2 systematic reviews with 18 RCTs and 18 observational studies34,149Mobile health interventions for prenatal, birth, and postnatal period in low- and middle-income countries
Telemonitoring and teleconsulting (12 studies)

Tapia-Conyer et al [75]RCT153Wireless antepartum maternal-fetal monitoring

Pflugeisen et al [74]Non-RCT1058Prenatal care with virtual visits and home measurements

Ivey et al [99]Prospective cohort155Teleconsultation with tertiary center

Cuneo et al [100]Prospective cohort125Home fetal heart monitoring for anti-SSA+c patients

Rauf et al [73]Prospective cohort70Fetal monitoring system for induction of labor

Krishnamurti et al [101]Prospective cohort16Smartphone app with information and symptom scores

Rhoads et al [102]Non-RCT50Telemonitoring of postpartum hypertension

Kerner et al [77]Feasibility study36Self-administered fetal heart rate monitoring

Marko et al [103]Feasibility study8Remote monitored pregnancy care (blood pressure, weight)

Marko et al [76]Controlled trial100Prenatal care with app and telemonitoring

Lanssens et al [104]Retrospective cohort166Remote monitoring of hypertension in pregnancy

Pflugeisen et al [105]Cross sectional171Satisfaction with virtual obstetric care

aRCT: randomized controlled trial.

bSMS: short message services.

cAnti-SSA: Anti-Sjögren’s-syndrome-related antigen A.

Mental Health

Electronic mental health has already proven to be successful in general population mental health management [56]. In 16 studies, the applicability on screening for and treatment of postpartum depression was investigated (Table 3). The prevalence of postpartum depression is 3%-15%. These women are reluctant to seek medical attention despite the heavy burden of disease, most notably because of the fear of their child being taken away from them [57,58]. Screening with telephone (alpha coefficients of .72-.94), app (sensitivity 72% and specificity 73%), and iPads were found feasible and acceptable [58-60]. eHealth programs (eg, online sessions based on cognitive behavior therapy) effectuate significant reductions in the depression scales and on symptom scores compared with treatment as usual [61-64]. Besides this significant effect size favoring eHealth, in 1 intervention group, the depression scores reduced also more quickly compared with the waiting list comparator group [63]. Perceptions of peer and social support significantly improved, and higher support was significantly related with lower depression symptoms [65]. An antenatal, first trimester eHealth intervention on depressive symptoms showed 80% intervention response and 60% remission (n=12) [63].

Low- and Middle-Income Countries

Limited resources and poor information are still leading to preventable maternal and neonatal deaths in low- and middle-income countries. The availability of mobile phones (in Africa and South-East Asia over 69%-90%) gives rise to the implementation of eHealth interventions and remote care. For more detailed information in this distinct population where eHealth is widely used, we refer to 2 recently published systematic reviews (Table 3). In summary, the interventions did increase antenatal care attendance, facility and service utilization, skilled support at birth, and vaccination rate [66]. Most of the included studies were of poor methodological quality or did not assess health outcomes [67]. Insufficient information was provided to evaluate the impact of eHealth solutions on maternal and fetal outcomes in these countries [67].

Telemonitoring and Teleconsulting

Telemonitoring of pregnancy is perceived to be one of the most promising answers to the possibilities of eHealth in pregnancy. Several hardware and software systems involving more complex remote monitoring are described lately (Table 3). An integrated system for maternal monitoring of glucose, weight, pulse and blood pressure, and a chat feature for clinician-patient contact is now in test [68]. Yi et al developed an Android-based mobile terminal for wireless fetal monitoring and uterine contractions tracking [69]. Using this system, patients in rural areas are provided with telemonitoring without traveling or hospitalization. Several other telemonitoring devices for cardiotocography have been tested in pilot settings or prospective cohorts and found feasible [70-72]. Currently, the effects of maternal and fetal telemonitoring in high-risk pregnancies on outcome, satisfaction, and costs are under research compared with hospital admission (the HOTEL trial, registered under #NTR6076). In a pilot with remote monitoring with transabdominal fetal electrocardiography (f-ECG) after induction with dinoprostone pessaries (n=70), successful monitoring was obtained in 89% [73]. Three women were recalled to the hospital due to suspicious f-ECG, of which in 2 cases caesarean section was indicated. A Virtual Obstetric Care program with normal visits combined with teleconferencing visits for low-risk pregnancy showed no increased risks in health outcomes besides an increase in preeclampsia diagnosis [74]. Another demonstration project describes a promising system of a wirelessly enabled maternal-fetal monitoring system MiBebe, used for the improvement of perinatal care in rural regions in Mexico. In the group of 153 high-risk pregnancies, the remote monitoring in 74 patients resulted in markedly increased adherence to antenatal visits with no adverse health outcomes compared with usual care [75]. One pilot study describes an alternative prenatal care schedule, including an integrated technology platform (mobile app, wireless weight scale, and blood pressure cuff), leading to a 43% reduction in outpatient visits (8 vs 14 visits) [76]. There was an increase in satisfaction and patient engagement and no change in perinatal outcome despite the decrease in face-to-face contact [76]. Remote monitoring and consultation can potentially reduce outpatient visits for antenatal consultation as well as hospitalization for certain clinical reasons. We see this in managing gestational diabetes with glucose monitoring but also in fetal monitoring for fetal growth restriction [53,77]. A model of cost-effectiveness analysis in a tertiary hospital (Ghent, Belgium) predicted a cost-reduction of 145,822 euros per year achieved by introducing home monitoring in high-risk pregnancy [78].

Patient and Caregiver Experience

Examining patients’ satisfaction with eHealth interventions, users describe high convenience and acceptance resulting in more patient activation and education. Patients report less concerns and anxiety and are comfortable with fewer clinic visits. Satisfaction rates vary between 86% and 95% in e-mental health studies and 90% (46/51) in home-monitored induction patients, who were very glad to stay in their own homely ambience as long as possible [73,79].

On the health care providers’ point of view, adaptation of obstetricians and midwives to eHealth solutions has not been widely described. Only 1 qualitative study interviewed 12 health care providers in obstetric departments. Concerns were raised on implementation barriers and potential medico-legal risks, but if addressed properly, implementation was considered feasible. Some clinicians admitted to have insufficient familiarity and skill with eHealth limiting their engagement and comprehension of the possibilities that eHealth technologies can confer to perinatal care. Overall, these clinicians regarded telemedicine as an additional parallel service rather than integrated into the antenatal care model [27].

Principal Findings

By providing this overview of the literature, we aimed to assess the applicability, advantages, and limitations of the use of eHealth in perinatal care. This review showed that eHealth interventions have a very broad, multilevel field of application focused on perinatal care in all its aspects. Most of the reviewed 71 articles were published after 2013, suggesting this novel type of care is an important topic of clinical and scientific relevance. Women of reproductive age seem to be interested in eHealth, as shown by their frequent use of smartphone, internet and apps, and searches for pregnancy information. Most health outcomes for perinatal eHealth interventions were generally positive, either resulting in positive effects (lifestyle, mental health) or providing multiple advantages while health outcomes were found equal (diabetes care). The implementation of telemonitoring was not studied extensively, but research provided important effects and advantages on facilitation of new care models. Patient and care provider satisfaction with eHealth interventions rates are generally good, with rates up to 95%.

Additional Considerations

Despite the promising preliminary results as reviewed above, research in eHealth has progressed much slower than developments in the health technology industry. A great amount of the reviewed articles on this subject addressed more than health outcomes or satisfaction rates alone. Advances in (implementation of) apps and devices and patient-generated data are retained by legal and financial concerns. Possible privacy risks involve a lack of control to collection of data and the use by third parties afterwards.

In the United States, eHealth legislation, secured in the Fair Information Practice principles (part of the Health Insurance Portability and Accountability Act), is lacking protection for endpoint users: the patients. End-to-end data encryption can be used to protect the useful patient data. Combined with authentication and access control mechanisms for patients as well as care providers, eHealth technologies can further enhance final security control [106]. The development of the Telemedicine for Medicare Act of 2015 may accelerate the removal of barriers and limitations regarding use of telehealth between different states in the United States [107].

In the framework of European law, eHealth is simultaneously a health care service and an information service with corresponding legislation [108]. eHealth developers have to mind general legislation regarding privacy protection (Dir 95/46/EC, Arts 8-12), electronic identification services, e-Commerce directive (eg, online contracting), safety requirements of medical devices, and general product safety and liability requirements. In answer to the interstate developments in eHealth care, the Cross Border Directive was initiated in 2011 in the European Union (EU). The objective of the initiatives within this directive is to turn telemedicine into a standard medical service, accessible to every European patient and fully covered by the respective social security system. Difficulties arise on liability and creating uniform rules in the EU, as member states have very intrinsic differences in national rules on health care, privacy, and liability. One advice would be for each member state to provide a legal framework for telemedicine, whereas the role of the EU would be limited to regulation [108].

The costs associated with development, purchase, and maintenance of eHealth equipment have dropped in recent years due to technological advancements [107]. Primary investments to implement eHealth in perinatal care are now attributed to personnel costs for both providers and technical support. However, to deliver care with the help of eHealth can also create savings on personnel costs and clinic visits. A systematic review of economic evaluation in telehealth solutions concluded that 29 out of 39 studies (74%) reported cost-effective, economically beneficial eHealth interventions in different conditions and diseases. The conclusion highlighted the fact that many studies did not report all recommended economic outcome items, leading to inconsistent analyses [109].

The challenges for reimbursement are delaying the widespread adoption of eHealth in all ranges of sections of hospital care. Coverage is fragmented, varying at level of country, within hospitals in the same country and within different specialties of health care [29]. Health insurance companies seem to be inclined to cover only well-researched eHealth interventions with according economic evaluations. The use of low-risk, inexpensive care models can operate as opportunities to objectify possible reduction in health care costs.

Advantages and disadvantages of eHealth implementation in perinatal care.


  • Patient satisfaction
  • Patient engagement
  • Fewer clinic visits
  • Clinician satisfaction
  • Remote monitoring
  • Access to care in low- and middle-income countries


  • Reimbursement
  • Legal issues
  • Technical issues


  • Impact on health outcome
  • Impact on costs
  • Limited A-level evidence
Textbox 1. Advantages and disadvantages of eHealth implementation in perinatal care.

Successes will motivate policy makers and drive the insurance market for additional coverage. Rigorous medical evidence can act as an extra stimulant; however, the duration and costs of designs and trials need to be taken into consideration [107].

Conclusion and Future Perspectives

This review provided an overview of eHealth as the next-generation perinatal care. Textbox 1 provides a condensed summary of the advantages (as described in Principal Findings) and disadvantages (as described in Additional Considerations) of the implementation of eHealth in perinatal care. If eHealth is to achieve its full potential, it should attain all domains of quality in care including safety, timeliness, effectiveness, efficiency, and patient centeredness. Cost-effectiveness assessment is needed to rationalize embracement and reimbursement. Policy makers should consider the international frameworks of legislation to support and implement this new form of care.

We accentuate that more research is needed, including economic evaluation of eHealth interventions. Growing engagement of calls for funding have responded: more large funding associations focus on the use of eHealth, warranting the qualitative impact of the studies in the application designs [110]. In addition, the potential of technology raised a nearly quadrupled amount of money in venture capital funding, from US $1.1 billion in 2011 to US $4.3 billion in 2015 [111].

Despite the challenges of privacy, liability, and costs, eHealth is very likely to disperse globally in the next decade. Some even state health care is approaching a tipping point [112]. The current shift to patient-centered care and increased patient empowerment underlines the need for revising current medical practice. eHealth has the potential to be integrated into standard care and deliver a revolution in perinatal health.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Search strategy and flow diagram.

PDF File (Adobe PDF File), 123KB

  1. World Health Organization. mHealth: New Horizons for Health Through Mobile Technologies. Global Observatory for eHealth Series, Volume 3. Geneva, Switzerland: WHO; 2011.
  2. Lettieri E, Fumagalli LP, Radaelli G, Bertele' P, Vogt J, Hammerschmidt R, et al. Empowering patients through eHealth: a case report of a pan-European project. BMC Health Serv Res 2015 Aug 05;15:309 [FREE Full text] [CrossRef] [Medline]
  3. Fumagalli LP, Radaelli G, Lettieri E, Bertele' P, Masella C. Patient empowerment and its neighbours: clarifying the boundaries and their mutual relationships. Health Policy 2015 Mar;119(3):384-394. [CrossRef] [Medline]
  4. Jones N, Corrigan PW, James D, Parker J, Larson N. Peer support, self-determination, and treatment engagement: a qualitative investigation. Psychiatr Rehabil J 2013 Sep;36(3):209-214. [CrossRef] [Medline]
  5. Barello S, Triberti S, Graffigna G, Libreri C, Serino S, Hibbard J, et al. eHealth for patient engagement: a systematic review. Front Psychol 2015;6:2013 [FREE Full text] [CrossRef] [Medline]
  6. Schwappach DL. Review: engaging patients as vigilant partners in safety: a systematic review. Med Care Res Rev 2010 Apr;67(2):119-148. [CrossRef] [Medline]
  7. Wallwiener S, Müller M, Doster A, Laserer W, Reck C, Pauluschke-Fröhlich J, et al. Pregnancy eHealth and mHealth: user proportions and characteristics of pregnant women using web-based information sources-a cross-sectional study. Arch Gynecol Obstet 2016 Nov;294(5):937-944. [CrossRef] [Medline]
  8. Bert F, Gualano MR, Brusaferro S, De Vito E, de Waure C, La Torre G, et al. Pregnancy e-health: a multicenter Italian cross-sectional study on Internet use and decision-making among pregnant women. J Epidemiol Community Health 2013 Dec 01;67(12):1013-1018. [CrossRef] [Medline]
  9. Johnson SA. 'Intimate mothering publics': comparing face-to-face support groups and Internet use for women seeking information and advice in the transition to first-time motherhood. Cult Health Sex 2015;17(2):237-251. [CrossRef] [Medline]
  10. Dutch Society of Obstetrics and Gynaecology. Vision Document 2011: Integrale verloskundige zorg in Nederland. Een stap verder. 2011.   URL: [accessed 2018-05-03] [WebCite Cache]
  11. Rodger D, Skuse A, Wilmore M, Humphreys S, Dalton J, Flabouris M, et al. Pregnant women's use of information and communications technologies to access pregnancy-related health information in South Australia. Aust J Prim Health 2013;19(4):308-312. [CrossRef] [Medline]
  12. Sayakhot P, Carolan-Olah M. Internet use by pregnant women seeking pregnancy-related information: a systematic review. BMC Pregnancy Childbirth 2016 Mar 28;16:65 [FREE Full text] [CrossRef] [Medline]
  13. Scaioli G, Bert F, Galis V, Brusaferro S, De Vito E, La Torre G, et al. Pregnancy and internet: sociodemographic and geographic differences in e-health practice. Results from an Italian multicenter study. Public Health 2015 Sep;129(9):1258-1266. [CrossRef] [Medline]
  14. Lee Y, Moon M. Utilization and content evaluation of mobile applications for pregnancy, birth, and child care. Healthc Inform Res 2016 Apr;22(2):73-80 [FREE Full text] [CrossRef] [Medline]
  15. Walker MG, Windrim C, Ellul KN, Kingdom JC. Web-based education for placental complications of pregnancy. J Obstet Gynaecol Can 2013 Apr;35(4):334-339. [CrossRef] [Medline]
  16. Wade VK, Karnon J, Elshaug AG, Hiller JE. A systematic review of economic analyses of telehealth services using real time video communication. BMC Health Serv Res 2010 Aug 10;10:233 [FREE Full text] [CrossRef] [Medline]
  17. Narasimhulu DM, Karakash S, Weedon J, Minkoff H. Patterns of internet use by pregnant women, and reliability of pregnancy-related searches. Matern Child Health J 2016 Dec;20(12):2502-2509. [CrossRef] [Medline]
  18. Gao LL, Larsson M, Luo SY. Internet use by Chinese women seeking pregnancy-related information. Midwifery 2013 Jul;29(7):730-735. [CrossRef] [Medline]
  19. Knight-Agarwal C, Davis DL, Williams L, Davey R, Cox R, Clarke A. Development and pilot testing of the Eating4two mobile phone app to monitor gestational weight gain. JMIR Mhealth Uhealth 2015 Jun 05;3(2):e44 [FREE Full text] [CrossRef] [Medline]
  20. Buultjens M, Robinson P, Milgrom J. Online resources for new mothers: opportunities and challenges for perinatal health professionals. J Perinat Educ 2012;21(2):99-111 [FREE Full text] [CrossRef] [Medline]
  21. Vlemmix F, Warendorf J, Rosman A, Kok M, Mol B, Morris J, et al. Decision aids to improve informed decision-making in pregnancy care: a systematic review. BJOG 2013 Feb;120(3):257-266 [FREE Full text] [CrossRef] [Medline]
  22. Ledford CJ, Canzona MR, Cafferty LA, Hodge JA. Mobile application as a prenatal education and engagement tool: a randomized controlled pilot. Patient Educ Couns 2016 Apr;99(4):578-582. [CrossRef] [Medline]
  23. Bush J, Barlow DE, Echols J, Wilkerson J, Bellevin K. Impact of a mobile health application on user engagement and pregnancy outcomes among Wyoming Medicaid members. Telemed J E Health 2017 Nov;23(11):891-898 [FREE Full text] [CrossRef] [Medline]
  24. Peragallo UR, Berger AA, Ivins AA, Beckham A, Thorp Jr JM, Nicholson WK. Internet use and access among pregnant women via computer and mobile phone: implications for delivery of perinatal care. JMIR Mhealth Uhealth 2015 Mar 30;3(1):e25 [FREE Full text] [CrossRef] [Medline]
  25. Lupton D. The use and value of digital media for information about pregnancy and early motherhood: a focus group study. BMC Pregnancy Childbirth 2016 Dec 19;16(1):171 [FREE Full text] [CrossRef] [Medline]
  26. Goetz M, Müller M, Matthies LM, Hansen J, Doster A, Szabo A, et al. Perceptions of patient engagement applications during pregnancy: a qualitative assessment of the patient's perspective. JMIR Mhealth Uhealth 2017 May 26;5(5):e73 [FREE Full text] [CrossRef] [Medline]
  27. Willcox J, van der Pligt P, Ball K, Wilkinson SA, Lappas M, McCarthy EA, et al. Views of women and health professionals on mhealth lifestyle interventions in pregnancy: a qualitative investigation. JMIR Mhealth Uhealth 2015 Oct 28;3(4):e99 [FREE Full text] [CrossRef] [Medline]
  28. Mackert M, Guadagno M, Donovan E, Whitten P. Including men in prenatal health: the potential of e-health to improve birth outcomes. Telemed J E Health 2015 Mar;21(3):207-212. [CrossRef] [Medline]
  29. Schwamm LH, Chumbler N, Brown E, Fonarow GC, Berube D, Nystrom K, et al. Recommendations for the implementation of telehealth in cardiovascular and stroke care: a policy statement from the American Heart Association. Circulation 2017 Feb 14;135(7):e24-e44 [FREE Full text] [CrossRef] [Medline]
  30. Muktabhant B, Lawrie TA, Lumbiganon P, Laopaiboon M. Diet or exercise, or both, for preventing excessive weight gain in pregnancy. Cochrane Database Syst Rev 2015 Jun 15(6):CD007145. [CrossRef] [Medline]
  31. Fell DB, Joseph KS, Armson BA, Dodds L. The impact of pregnancy on physical activity level. Matern Child Health J 2009 Sep;13(5):597-603. [CrossRef] [Medline]
  32. Di Mascio D, Magro-Malosso ER, Saccone G, Marhefka GD, Berghella V. Exercise during pregnancy in normal-weight women and risk of preterm birth: a systematic review and meta-analysis of randomized controlled trials. Am J Obstet Gynecol 2016 Nov;215(5):561-571. [CrossRef] [Medline]
  33. Waring ME, Moore Simas TA, Xiao RS, Lombardini LM, Allison JJ, Rosal MC, et al. Pregnant women's interest in a website or mobile application for healthy gestational weight gain. Sex Reprod Healthc 2014 Dec;5(4):182-184 [FREE Full text] [CrossRef] [Medline]
  34. Hayman M, Reaburn P, Browne M, Vandelanotte C, Alley S, Short CE. Feasibility, acceptability and efficacy of a web-based computer-tailored physical activity intervention for pregnant women - the Fit4Two randomised controlled trial. BMC Pregnancy Childbirth 2017 Dec 23;17(1):96 [FREE Full text] [CrossRef] [Medline]
  35. Soltani H, Duxbury AM, Arden MA, Dearden A, Furness PJ, Garland C. Maternal obesity management using mobile technology: a feasibility study to evaluate a text messaging based complex intervention during pregnancy. J Obes 2015;2015:814830 [FREE Full text] [CrossRef] [Medline]
  36. Choi J, Lee JH, Vittinghoff E, Fukuoka Y. mHealth physical activity intervention: a randomized pilot study in physically inactive pregnant women. Matern Child Health J 2016 May;20(5):1091-1101 [FREE Full text] [CrossRef] [Medline]
  37. Willcox JC, Wilkinson SA, Lappas M, Ball K, Crawford D, McCarthy EA, et al. A mobile health intervention promoting healthy gestational weight gain for women entering pregnancy at a high body mass index: the txt4two pilot randomised controlled trial. BJOG 2017 Oct;124(11):1718-1728. [CrossRef] [Medline]
  38. Agarwal S, Labrique A. Newborn health on the line: the potential mHealth applications. J Am Med Assoc 2014 Jul 16;312(3):229-230. [CrossRef] [Medline]
  39. Graham ML, Uesugi KH, Niederdeppe J, Gay GK, Olson CM. The theory, development, and implementation of an e-intervention to prevent excessive gestational weight gain: e-Moms Roc. Telemed J E Health 2014 Dec;20(12):1135-1142 [FREE Full text] [CrossRef] [Medline]
  40. Tong VT, Dietz PM, Morrow B, D'Angelo DV, Farr SL, Rockhill KM, Centers for Disease Control and Prevention (CDC). Trends in smoking before, during, and after pregnancy--Pregnancy Risk Assessment Monitoring System, United States, 40 sites, 2000-2010. MMWR Surveill Summ 2013 Nov 08;62(6):1-19 [FREE Full text] [Medline]
  41. McLeod D, Pullon S, Cookson T. Factors that influence changes in smoking behaviour during pregnancy. N Z Med J 2003 May 02;116(1173):U418. [Medline]
  42. Heminger CL, Schindler-Ruwisch JM, Abroms LC. Smoking cessation support for pregnant women: role of mobile technology. Subst Abuse Rehabil 2016;7:15-26 [FREE Full text] [CrossRef] [Medline]
  43. National Institute for Health and Care Excellence. 2015. Diabetes in pregnancy: management from preconception to the postnatal period   URL: [accessed 2018-05-03] [WebCite Cache]
  44. Wong VW, Jalaludin B. Gestational diabetes mellitus: who requires insulin therapy? Aust N Z J Obstet Gynaecol 2011 Oct;51(5):432-436. [CrossRef] [Medline]
  45. Khorshidi Roozbahani R, Geranmayeh M, Hantoushzadeh S, Mehran A. Effects of telephone follow-up on blood glucose levels and postpartum screening in mothers with Gestational Diabetes Mellitus. Med J Islam Repub Iran 2015;29:249 [FREE Full text] [Medline]
  46. Chilelli NC, Dalfrà MG, Lapolla A. The emerging role of telemedicine in managing glycemic control and psychobehavioral aspects of pregnancy complicated by diabetes. Int J Telemed Appl 2014;2014:621384 [FREE Full text] [CrossRef] [Medline]
  47. Homko CJ, Santamore WP, Whiteman V, Bower M, Berger P, Geifman-Holtzman O, et al. Use of an internet-based telemedicine system to manage underserved women with gestational diabetes mellitus. Diabetes Technol Ther 2007 Jun;9(3):297-306. [CrossRef] [Medline]
  48. Homko CJ, Deeb LC, Rohrbacher K, Mulla W, Mastrogiannis D, Gaughan J, et al. Impact of a telemedicine system with automated reminders on outcomes in women with gestational diabetes mellitus. Diabetes Technol Ther 2012 Jul;14(7):624-629 [FREE Full text] [CrossRef] [Medline]
  49. Carral F, Ayala Mdel C, Fernández JJ, González C, Piñero A, García G, et al. Web-based telemedicine system is useful for monitoring glucose control in pregnant women with diabetes. Diabetes Technol Ther 2015 May;17(5):349-354. [CrossRef] [Medline]
  50. Given JE, Bunting BP, O'Kane MJ, Dunne F, Coates VE. Tele-Mum: a feasibility study for a randomized controlled trial exploring the potential for telemedicine in the diabetes care of those with gestational diabetes. Diabetes Technol Ther 2015 Dec;17(12):880-888. [CrossRef] [Medline]
  51. Mackillop L, Loerup L, Bartlett K, Farmer A, Gibson OJ, Hirst JE, et al. Development of a real-time smartphone solution for the management of women with or at high risk of gestational diabetes. J Diabetes Sci Technol 2014 Nov;8(6):1105-1114 [FREE Full text] [CrossRef] [Medline]
  52. Pérez-Ferre N, Galindo M, Fernández MD, Velasco V, Runkle I, de la Cruz MJ, et al. The outcomes of gestational diabetes mellitus after a telecare approach are not inferior to traditional outpatient clinic visits. Int J Endocrinol 2010;2010:386941 [FREE Full text] [CrossRef] [Medline]
  53. Rasekaba TM, Furler J, Blackberry I, Tacey M, Gray K, Lim K. Telemedicine interventions for gestational diabetes mellitus: a systematic review and meta-analysis. Diabetes Res Clin Pract 2015 Oct;110(1):1-9. [CrossRef] [Medline]
  54. Ming WK, Mackillop LH, Farmer AJ, Loerup L, Bartlett K, Levy JC, et al. Telemedicine technologies for diabetes in pregnancy: a systematic review and meta-analysis. J Med Internet Res 2016 Nov 09;18(11):e290 [FREE Full text] [CrossRef] [Medline]
  55. Noctor E, Dunne FP. Type 2 diabetes after gestational diabetes: the influence of changing diagnostic criteria. World J Diabetes 2015 Mar 15;6(2):234-244 [FREE Full text] [CrossRef] [Medline]
  56. Andrews G, Cuijpers P, Craske MG, McEvoy P, Titov N. Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: a meta-analysis. PLoS One 2010 Oct 13;5(10):e13196 [FREE Full text] [CrossRef] [Medline]
  57. Beck CT. The lived experience of postpartum depression: a phenomenological study. Nurs Res 1992;41(3):166-170. [Medline]
  58. Mitchell AM, Mittelstaedt ME, Schott-Baer D. Postpartum depression: the reliability of telephone screening. MCN Am J Matern Child Nurs 2006;31(6):382-387. [Medline]
  59. Jiménez-Serrano S, Tortajada S, García-Gómez JM. A mobile health application to predict postpartum depression based on machine learning. Telemed J E Health 2015 Jul;21(7):567-574. [CrossRef] [Medline]
  60. Kingston D, Austin MP, Veldhuyzen van Zanten S, Harvalik P, Giallo R, McDonald SD, et al. Pregnant women's views on the feasibility and acceptability of web-based mental health e-screening versus paper-based screening: a randomized controlled trial. J Med Internet Res 2017 Apr 07;19(4):e88 [FREE Full text] [CrossRef] [Medline]
  61. Lee EW, Denison FC, Hor K, Reynolds RM. Web-based interventions for prevention and treatment of perinatal mood disorders: a systematic review. BMC Pregnancy Childbirth 2016 Feb 29;16:38 [FREE Full text] [CrossRef] [Medline]
  62. Posmontier B, Neugebauer R, Stuart S, Chittams J, Shaughnessy R. Telephone-administered interpersonal psychotherapy by nurse-midwives for postpartum depression. J Midwifery Womens Health 2016 Jul;61(4):456-466. [CrossRef] [Medline]
  63. Ashford MT, Olander EK, Ayers S. Computer- or web-based interventions for perinatal mental health: a systematic review. J Affect Disord 2016 Jun;197:134-146. [CrossRef] [Medline]
  64. Lau Y, Htun TP, Wong SN, Tam WS, Klainin-Yobas P. Therapist-supported internet-based cognitive behavior therapy for stress, anxiety, and depressive symptoms among postpartum women: a systematic review and meta-analysis. J Med Internet Res 2017 Apr 28;19(4):e138 [FREE Full text] [CrossRef] [Medline]
  65. Letourneau N, Secco L, Colpitts J, Aldous S, Stewart M, Dennis CL. Quasi-experimental evaluation of a telephone-based peer support intervention for maternal depression. J Adv Nurs 2015 Jul;71(7):1587-1599. [CrossRef] [Medline]
  66. Sondaal SF, Browne JL, Amoakoh-Coleman M, Borgstein A, Miltenburg AS, Verwijs 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 [FREE Full text] [CrossRef] [Medline]
  67. Lee SH, Nurmatov UB, Nwaru BI, 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 Jun;6(1):010401 [FREE Full text] [CrossRef] [Medline]
  68. Robu A, Gauca B, Crisan-Vida M, Stoicu-Tivadar L. Integrated system for monitoring and prevention in obstetrics-gynaecology. Stud Health Technol Inform 2016;221:8-12. [Medline]
  69. Xi H, Gan G, Zhang H, Chen C. [Design of Smart Care Tele-Monitoring System for Mother and Fetus]. Zhongguo Yi Liao Qi Xie Za Zhi 2015 Mar;39(2):102-104. [Medline]
  70. Hod M, Kerner R. Telemedicine for antenatal surveillance of high-risk pregnancies with ambulatory and home fetal heart rate monitoring--an update. J Perinat Med 2003;31(3):195-200. [CrossRef] [Medline]
  71. Niţulescu A, Crişan-Vida M, Stoicu-Tivadar L, Bernad E. Integrated wireless sensor network for monitoring pregnant women. Stud Health Technol Inform 2015;210:354-358. [Medline]
  72. Vermeulen-Giovagnoli B, Peters C, van der Hout-van der Jagt MB, Mischi M, van Pul C, Cottaar EJ, et al. The development of an obstetric tele-monitoring system. Conf Proc IEEE Eng Med Biol Soc 2015;2015:177-180. [CrossRef] [Medline]
  73. Rauf Z, O'Brien E, Stampalija T, Ilioniu FP, Lavender T, Alfirevic Z. Home labour induction with retrievable prostaglandin pessary and continuous telemetric trans-abdominal fetal ECG monitoring. PLoS One 2011;6(11):e28129 [FREE Full text] [CrossRef] [Medline]
  74. Pflugeisen BM, McCarren C, Poore S, Carlile M, Schroeder R. Virtual visits: managing prenatal care with modern technology. MCN Am J Matern Child Nurs 2016;41(1):24-30. [CrossRef] [Medline]
  75. Tapia-Conyer R, Lyford S, Saucedo R, Casale M, Gallardo H, Becerra K, et al. Improving perinatal care in the rural regions worldwide by wireless enabled antepartum fetal monitoring: a demonstration project. Int J Telemed Appl 2015;2015:794180 [FREE Full text] [CrossRef] [Medline]
  76. Marko K, Ganju N, Brown J, Benham J, Gaba ND. Remote prenatal care monitoring with digital health tools can reduce visit frequency while improving satisfaction. Obstet Gynecol 2016;127(1):1. [CrossRef]
  77. Kerner R, Yogev Y, Belkin A, Ben-Haroush A, Zeevi B, Hod M. Maternal self-administered fetal heart rate monitoring and transmission from home in high-risk pregnancies. Int J Gynaecol Obstet 2004 Jan;84(1):33-39. [Medline]
  78. Buysse H, De Moor MG, Van Maele G, Baert E, Thienpont G, Temmerman M. Cost-effectiveness of telemonitoring for high-risk pregnant women. Int J Med Inform 2008 Jul;77(7):470-476. [CrossRef] [Medline]
  79. O'Brien E, Rauf Z, Alfirevic Z, Lavender T. Women's experiences of outpatient induction of labour with remote continuous monitoring. Midwifery 2013 Apr;29(4):325-331. [CrossRef] [Medline]
  80. Pollak KI, Alexander SC, Bennett G, Lyna P, Coffman CJ, Bilheimer A, et al. Weight-related SMS texts promoting appropriate pregnancy weight gain: a pilot study. Patient Educ Couns 2014 Nov;97(2):256-260 [FREE Full text] [CrossRef] [Medline]
  81. Graham ML, Strawderman MS, Demment M, Olson CM. Does usage of an ehealth intervention reduce the risk of excessive gestational weight gain? Secondary analysis from a randomized controlled trial. J Med Internet Res 2017 Jan 09;19(1):e6 [FREE Full text] [CrossRef] [Medline]
  82. Huberty JL, Buman MP, Leiferman JA, Bushar J, Hekler EB, Adams MA. Dose and timing of text messages for increasing physical activity among pregnant women: a randomized controlled trial. Transl Behav Med 2017 Jun;7(2):212-223 [FREE Full text] [CrossRef] [Medline]
  83. Lewis BA, Martinson BC, Sherwood NE, Avery MD. A pilot study evaluating a telephone-based exercise intervention for pregnant and postpartum women. J Midwifery Womens Health 2011;56(2):127-131. [CrossRef] [Medline]
  84. Guo SH, Lee CW, Tsao CM, Hsing HC. A social media-based mindful yoga program for pregnant women in Taiwan. Stud Health Technol Inform 2016;225:621-622. [Medline]
  85. Kruger DF, White K, Galpern A, Mann K, Massirio A, McLellan M, et al. Effect of modem transmission of blood glucose data on telephone consultation time, clinic work flow, and patient satisfaction for patients with gestational diabetes mellitus. J Am Acad Nurse Pract 2003 Aug;15(8):371-375. [Medline]
  86. Dalfrà MG, Nicolucci A, Lapolla A, TISG. The effect of telemedicine on outcome and quality of life in pregnant women with diabetes. J Telemed Telecare 2009;15(5):238-242. [CrossRef] [Medline]
  87. Wojcicki JM, Ladyzynski P, Foltynski P. What we can really expect from telemedicine in intensive diabetes treatment: 10 years later. Diabetes Technol Ther 2013 Mar;15(3):260-268. [CrossRef] [Medline]
  88. Nicholson WK, Beckham AJ, Hatley K, Diamond M, Johnson LS, Green SL, et al. The Gestational Diabetes Management System (GooDMomS): development, feasibility and lessons learned from a patient-informed, web-based pregnancy and postpartum lifestyle intervention. BMC Pregnancy Childbirth 2016 Sep 21;16(1):277 [FREE Full text] [CrossRef] [Medline]
  89. Ganapathy R, Grewal A, Castleman JS. Remote monitoring of blood pressure to reduce the risk of preeclampsia related complications with an innovative use of mobile technology. Pregnancy Hypertens 2016 Oct;6(4):263-265. [CrossRef] [Medline]
  90. Harrison TN, Sacks DA, Parry C, Macias M, Ling Grant DS, Lawrence JM. Acceptability of virtual prenatal visits for women with gestational diabetes. Womens Health Issues 2017;27(3):351-355. [Medline]
  91. Milgrom J, Danaher BG, Gemmill AW, Holt C, Holt CJ, Seeley JR, et al. Internet cognitive behavioral therapy for women with postnatal depression: a randomized controlled trial of Mummoodbooster. J Med Internet Res 2016 Mar 07;18(3):e54 [FREE Full text] [CrossRef] [Medline]
  92. Ngai FW, Wong PW, Leung KY, Chau PH, Chung KF. The effect of telephone-based cognitive-behavioral therapy on postnatal depression: a randomized controlled trial. Psychother Psychosom 2015;84(5):294-303. [CrossRef] [Medline]
  93. Shamshiri Milani H, Azargashb E, Beyraghi N, Defaie S, Asbaghi T. Effect of telephone-based support on postpartum depression: a randomized controlled trial. Int J Fertil Steril 2015;9(2):247-253 [FREE Full text] [Medline]
  94. Fontein-Kuipers YJ, Nieuwenhuijze MJ, Ausems M, Budé L, de Vries R. Antenatal interventions to reduce maternal distress: a systematic review and meta-analysis of randomised trials. BJOG 2014 Mar;121(4):389-397 [FREE Full text] [CrossRef] [Medline]
  95. Broom MA, Ladley AS, Rhyne EA, Halloran DR. Feasibility and perception of using text messages as an adjunct therapy for low-income, minority mothers with postpartum depression. JMIR Ment Health 2015;2(1):e4 [FREE Full text] [CrossRef] [Medline]
  96. Figueiredo FP, Parada AP, Cardoso VC, Batista RF, Silva AA, Barbieri MA, et al. Postpartum depression screening by telephone: a good alternative for public health and research. Arch Womens Ment Health 2015 Jun;18(3):547-553. [CrossRef] [Medline]
  97. Pugh NE, Hadjistavropoulos HD, Fuchs CM. Internet therapy for postpartum depression: a case illustration of emailed therapeutic assistance. Arch Womens Ment Health 2014 Aug;17(4):327-337. [CrossRef] [Medline]
  98. Pineros-Leano M, Tabb K, Sears H, Meline B, Huang H. Clinic staff attitudes towards the use of mHealth technology to conduct perinatal depression screenings: a qualitative study. Fam Pract 2015 Apr;32(2):211-215. [CrossRef] [Medline]
  99. Ivey TL, Hughes D, Dajani NK, Magann EF. Antenatal management of at-risk pregnancies from a distance. Aust N Z J Obstet Gynaecol 2015 Feb;55(1):87-89. [CrossRef] [Medline]
  100. Cuneo BF, Moon-Grady AJ, Sonesson SE, Levasseur S, Hornberger L, Donofrio MT, et al. Heart sounds at home: feasibility of an ambulatory fetal heart rhythm surveillance program for anti-SSA-positive pregnancies. J Perinatol 2017 Mar;37(3):226-230. [CrossRef] [Medline]
  101. Krishnamurti T, Davis AL, Wong-Parodi G, Fischhoff B, Sadovsky Y, Simhan HN. Development and testing of the Myhealthypregnancy app: a behavioral decision research-based tool for assessing and communicating pregnancy risk. JMIR Mhealth Uhealth 2017 Apr 10;5(4):e42 [FREE Full text] [CrossRef] [Medline]
  102. Rhoads SJ, Serrano CI, Lynch CE, Ounpraseuth ST, Gauss CH, Payakachat N, et al. Exploring implementation of m-health monitoring in postpartum women with hypertension. Telemed J E Health 2017 Oct;23(10):833-841. [CrossRef] [Medline]
  103. Marko KI, Krapf JM, Meltzer AC, Oh J, Ganju N, Martinez AG, et al. Testing the feasibility of remote patient monitoring in prenatal care using a mobile app and connected devices: a prospective observational trial. JMIR Res Protoc 2016 Nov 18;5(4):e200 [FREE Full text] [CrossRef] [Medline]
  104. Lanssens D, Vandenberk T, Smeets CJ, De Cannière H, Molenberghs G, Van Moerbeke A, et al. Remote monitoring of hypertension diseases in pregnancy: a pilot study. JMIR Mhealth Uhealth 2017 Mar 09;5(3):e25 [FREE Full text] [CrossRef] [Medline]
  105. Pflugeisen BM, Mou J. Patient satisfaction with virtual obstetric care. Matern Child Health J 2017 Jul;21(7):1544-1551. [CrossRef] [Medline]
  106. Hall JL, McGraw D. For telehealth to succeed, privacy and security risks must be identified and addressed. Health Aff (Millwood) 2014 Feb;33(2):216-221. [CrossRef] [Medline]
  107. Dorsey ER, Topol EJ. State of Telehealth. N Engl J Med 2016 Jul 14;375(2):154-161. [CrossRef] [Medline]
  108. Raposo VL. Telemedicine: the legal framework (or the lack of it) in Europe. GMS Health Technol Assess 2016;12:Doc03 [FREE Full text] [CrossRef] [Medline]
  109. Iribarren SJ, Cato K, Falzon L, Stone PW. What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions. PLoS One 2017;12(2):e0170581 [FREE Full text] [CrossRef] [Medline]
  110. Kurzweil R. The Singularity Is Near: When Humans Transcend Biology. New York: Viking; 2005.
  111. Wang T, King E, Perman M, Tecco H. Rockhealth. Digital health funding: 2015 year in review   URL: [accessed 2018-05-03] [WebCite Cache]
  112. Gladwell M. The Tipping Point: How Little Things Can Make A Big Difference. US: Little, Brown; 2000.

Anti-SSA: Anti-Sjögren’s-syndrome-related antigen A
eHealth: electronic health
e-mental health: electronic mental health
EU: European Union
f-ECG: fetal electrocardiography
GDM: gestational diabetes mellitus
RCT: randomized controlled trial
SMS: short message service

Edited by G Eysenbach; submitted 02.11.17; peer-reviewed by M Ashford, T Rasekaba, S Wallwiener, S Aggarwal; comments to author 14.12.17; revised version received 19.01.18; accepted 10.03.18; published 05.06.18


©Josephus FM van den Heuvel, T Katrien Groenhof, Jan HW Veerbeek, Wouter W van Solinge, A Titia Lely, Arie Franx, Mireille N Bekker. Originally published in the Journal of Medical Internet Research (, 05.06.2018.

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