Table 2 highlights the strategies and barriers identified in the different phases of the eHealth lifestyle interventions. These phases involve design decisions, developing intervention content, and the willingness of individuals to use the intervention. In this section, we elaborate on these strategies to provide a detailed overview of the findings.

Some of the eHealth interventions included components of existing interventions or were adaptations of existing interventions. For instance, SmartMoms in WIC [36], Quit4baby [42], I-PREGNO [38], and Healthy Pregnancy: Step by Step [46] made adaptations to existing interventions to better meet the specific needs of their vulnerable target groups. They made these adaptations, such as creating a sense of community, by including support groups through Facebook based on recommendations provided by stakeholders [36,46]. In addition, Rhodes et al [45] and Huberty et al [55] developed their interventions as an extension to existing eHealth interventions or modalities and referred to existing eHealth modalities as well suited for the implementation and evaluation of novel eHealth lifestyle interventions.

Over half (17/29, 59%) of the studies used theoretical frameworks for behavior change, which contributed substantially to the design of their interventions. The frameworks used were social cognitive theory [37,39,40,42-44,50,52,57], the transtheoretical model of behavior change [15,39,41,46,47], the multiple health behavior change paradigm [34,35], the Behavior Change Wheel [48], the Fogg behavior model [45], and the health belief model [39]. Most studies (24/29, 83%) did not provide justifications for their selection of a particular behavior change framework. However, Kennedy et al [48] and Mauriello et al [15] selected their frameworks based on their effectiveness in previous studies. In addition, the Behavior Change Wheel was used for its ability to advance understanding of features in need of improvement [48]. The multiple health behavior change paradigm was chosen as a guiding framework due to its unique approach in addressing multiple health behaviors simultaneously through one intervention [34,35]. In addition, the researchers used clinical and best practice guidelines to inform the content of their interventions [41,42,50,52,57]. Furthermore, theoretical frameworks and guidelines were used to guide the development of the eHealth tools, including the Centre for eHealth Research Roadmap [48], the behavioral decision research paradigm [49], the Behavior Change Wheel [45], the Sanders and Stappers co-design framework [33], the Kaupapa Māori framework [33], and Noorbergen’s guidelines for co-design of mobile health (mHealth) systems [33].

Stakeholders provided valuable guidance to researchers in terms of the design and content of the eHealth lifestyle interventions. Stakeholders included, among others, the target group, research centers, academics specialized in different health domains, social service providers, literacy experts, and mHealth companies. For co-creation with the target group, the researchers used multiple methods, such as surveys [20,55], interviews [18,37,45,47-49,55,57], focus groups [19,37,38,45-47,53,56], user research [45-49], and monthly meetings [36], to ensure that their target group’s needs were met in terms of design, literacy, content, and usability of the eHealth lifestyle intervention. For instance, findings from interviews with the target group can indicate barriers that women encounter related to healthy lifestyle practices during pregnancy [57]. In turn, this knowledge can inform the design of an eHealth lifestyle intervention that overcomes these barriers.

However, collaborating with stakeholders can also entail challenges. For example, Flanagan et al [36] experienced a disparity between their mothers advisory group’s request to include more health markers in their trial and the limited enthusiasm for this adaptation from the scientific review panel. Similarly, Whittaker et al [53] encountered challenges in information sharing and estimating the in-kind costs of their initiative. They also faced confusion regarding defined roles and responsibilities among their partners and differing perspectives and priorities in transitioning to the next stage of their partnerships. Establishing and adhering to a set of guiding principles, comprising key intervention design objectives and features, could ensure that all stakeholders involved in the intervention design work toward a common vision [45].

Researchers provided devices to their participants for various purposes. Some researchers provided devices necessary for the delivery of the intervention [18,34,35], whereas others provided devices to collect data for evaluating the intervention’s effectiveness. The latter included a Fitbit to measure physical activity [36,43], a pedometer to track step counts [37], a piCO Smokerlyzer to measure breath carbon monoxide [41], and a digital scale to monitor gestational weight gain [27,37]. In addition to evaluating effectiveness, Herring et al [37] highlighted that providing devices might facilitate adherence to the intervention. However, personal reasons can influence the use of devices, such as not being able to wear a smartwatch during work [18].

Technical problems were expressed by participants in several studies (4/29, 14%) [18,19,34,48], often negatively influencing engagement. Multiple studies (4/29, 14%) provided technological support to prevent or help with technological issues [18,19,34,35]. For example, information was provided on how to use the device, download the app, and synchronize devices, and contact information for study coordinators was provided to help when the participant encountered technical issues. However, offering contact details for technological support alone may not be enough. For example, Dalton et al [19] provided phone numbers for technological support but found that 9% of the participants failed to report the problems they encountered while downloading the app. Leaving participants to manage the app on their own to assess its usability might have influenced the high dropout rate in their study [19].

In a few studies (4/29, 14%), the researchers mentioned attrition of participants after enrollment [18,19,34,35]. Reasons that might have contributed to this attrition were participants’ employment status [19], financial constraints [19], anxiety levels [19], and time constraints [18,19,34]. To retain participants, Mauriello et al [15] had participants engage with the intervention during their appointments, contacted participants who could not be reached in various ways to make them complete final assessments, and used incentives for completing a session. These strategies resulted in an impressive retention. The attrition in the study by Wambach et al [34] prompted the development of strategies to limit attrition in future research. These included enhancing staff training to focus on a clear and unrushed explanation of study requirements during recruitment, improving the description of study requirements on an advertising flyer, and including larger incentives. This resulted in a lower attrition rate, although it remained high at 25.8% [35].

The features (components that make up the eHealth intervention) included in the eHealth interventions are summarized in Table 3. Many interventions (6/20, 30%) included a feature in which participants could interact with others in their intervention, such as a chat room or Facebook page [19,34-37,48,52]. However, in one intervention, peer support was considered the least useful component by most of the participants [34]. Some interventions (2/20, 10%) actively included partners as a way of providing social support to pregnant women [38,45]. In addition, links to external sources of information were provided, which prevented the inclusion of too much information in the content of the intervention but also catered to those who were looking for more information about a certain topic [19,34,35,40,41,43,45,52,55,57]. Furthermore, although some interventions included weight trackers, Rhodes et al [45] decided not to include weight monitoring or weight-related messages based on their potential to demotivate their participants.

Some of the incorporated features used were specific to a limited number of interventions. For example, Krishnamurti et al [49] included Uber services in their intervention to provide free transportation to prenatal care appointments as transportation was revealed to be a barrier for their target group. Providing Uber transportation prevented missed appointments and was found to be cost saving. Song et al [20] were the only ones to include an automated, 2-way SMS text messaging system in their intervention to distribute pregnancy and health-related information and foster patient–health care provider interaction. Despite some frustrations regarding its ability to answer participants’ questions, the SMS text messaging system could promote health communication while offering psychological benefits as well [20]. However, it was suggested that the addition of more system-initiated SMS text messages could benefit women who are less comfortable with asking questions [20].

In the included studies, the researchers used multiple sites and strategies and involved various key persons to recruit vulnerable pregnant women (Table 4). Often, recruitment took place in a health care setting (19/29, 66%), and many studies (12/29, 41%) received support from health professionals for recruitment. Apart from face-to-face or phone recruitment (22/29, 76%), many studies used printed materials (11/29, 38%) or the internet (5/29, 17%) to promote their interventions. Printed materials were placed at sites frequently visited by pregnant women, such as schools and children’s retail stores.

Challenges related to participant recruitment were frequently encountered. One of these challenges was a limited number of eligible participants [15,18,20,34]. For example, Song et al [20] experienced challenges in recruiting pregnant women in their second and third trimesters due to a high rate of preterm labor within the low-income minority pregnant population. A second challenge in recruitment was due to eligible candidates’ reluctance to participate because of a lack of interest, time constraints, and concerns about sharing personal information [18,34,39]. Health care professionals and staff from non–health care organizations were often mentioned as facilitators in recruiting the target group, either providing assistance or taking full responsibility for the recruitment process [15,18,20,37,41,46-48,53,56], but sometimes this posed challenges as well [34,39]. For example, research personnel in the study by Wambach et al [34] received limited support from clinical staff during recruitment visits. Finally, resource aspects, including limited phone ownership [39], lack of access to an internet service [19,47], and phone operating systems that were incompatible with the eHealth intervention [19], hindered recruitment. In some studies (6/29, 21%), recruitment difficulties resulted in a small sample size [20,34,39-41,48].

Certain strategies were implemented to overcome recruitment challenges. For example, in response to the limited number of pregnant adolescents, Wambach et al [34] expanded their age range and the number of recruitment sites and included word-of-mouth recruitment. Second, in the study by Harris and Reynolds [41], research personnel conducted home visits to set up equipment and provide detailed training on its use, thereby enhancing accessibility for rural pregnant smokers. Furthermore, to overcome participants’ limited access to an internet or telephone service, Prochaska et al [47] encouraged providers of the intervention to have a computer kiosk at their centers for women to access the program.

Many studies (18/29, 62%) offered incentives to encourage participation in their trial, interview, focus group, or workshop [15,20,33,43,45,46,51,55,57]; attend study visits or video check-ins [18,36]; complete assessments, questionnaires, or interviews [20,38,49,50,56]; submit self-monitoring data [37] or bodily samples [42]; engage with the intervention [15,18,36,49]; or abstain from unhealthy behaviors [18,41]. The incentives provided in these studies included gift cards [15,20,33,36,37,41,42,46,50,57]; monetary compensation [38,41,45,49,55]; devices such as smartphones [20,49], scales [49], and smartwatches [43]; and health-related items such as yoga mats and prenatal vitamins [36].

Few studies (2/18, 11%) discussed the impact of the incentives they used. Nevertheless, Leavitt et al [51] observed a significant decline in the response rate after removing the reference to their incentive in their recruitment message. Furthermore, Harris and Reynolds [41] believed that their incentives enhanced participants’ motivation to quit smoking by the specified quit date. However, in the study by Joyce et al [18], qualitative interviews revealed mixed feelings toward financial incentives, where one participant stated that the financial incentive was encouraging to quit smoking, whereas another participant expressed that a motivational tailored message would be rewarding enough without financial rewards.

Assessment of implementation includes factors such as the successfulness and costs of intervention delivery. Most studies (18/29, 62%) did not describe how their eHealth lifestyle intervention was or would be implemented. However, some strategies in the implementation phase of the eHealth lifestyle interventions were identified, which are highlighted in Table 2.

A limited number of studies (5/29, 17%) described whether costs and funding acted as barriers or facilitators in the design or implementation phase of their eHealth lifestyle intervention. Nevertheless, in 7% (2/29) of the studies, budget constraints limited intervention development [46,48]. Furthermore, costs from financial incentives and loaned devices could be barriers to intervention implementation [41]. Implementing the intervention in a clinical setting was seen as a way to reduce costs [41]. Whittaker et al [53] mentioned how funding from and involvement of well-known national partners facilitated the national launch of their intervention. In addition, providing the intervention free of charge to vulnerable pregnant women could facilitate implementation [53,54].

A few strategies were highlighted in the studies to ensure the dissemination of their interventions. First, collaboration with stakeholders was frequently emphasized as vital for the successful implementation of the interventions. For example, the adoption of an intervention by a governmental program or by existing perinatal care services can facilitate widespread implementation and distribution [36,38]. The enthusiasm of stakeholders and the intervention’s relevance to them were mentioned as characteristics that should contribute to the ease of the dissemination of their interventions [15,47]. Second, a low workload for intervention deliverers was mentioned in multiple studies (3/29, 10%) as an important aspect of a feasible implementation [36,41,46]. Third, providing the intervention in a format that can be distributed via any internet-enabled device, either in health care settings or via a personal internet-enabled device at the convenience of its user, contributes to dissemination [15]. Furthermore, media appearance can result in a considerable increase in rates of enrollment in the intervention [53]. Finally, to develop an intervention that could be easily integrated into health care structures, dissemination issues should be considered from the start of the project [47].

A few research groups developed their interventions with the aim of integrating them into the current health care structures [36,38,46,47,49,52,56]. Several suggestions were given for this integration, such as women using the intervention before their consult and sharing a printed report of their results with their health care provider [47]. For this integration, it was important that the program be self-directed, require little to no staff training, and allow for low-cost and consistent delivery [46]. However, taking health care providers’ time and resource constraints into consideration, an intervention can be intentionally developed to be used outside of health care as well [37].

This scoping review provides a comprehensive overview of the applied strategies in 4 phases (design, reach, use, and implementation) of eHealth lifestyle interventions for vulnerable pregnant women. In addition, it highlights which barriers researchers encountered and which strategies acted as facilitators for these interventions. By identifying barriers and facilitators in current eHealth lifestyle interventions aimed at vulnerable pregnant women, our study generated insights into how to optimize eHealth lifestyle interventions for this population. As this scoping review covered a wide range of interventions, study designs, targeted health behaviors, and vulnerabilities, it also contributes to a nuanced understanding of the landscape of available eHealth lifestyle interventions for vulnerable pregnant women.

We included 29 articles describing 20 eHealth interventions in this review. These interventions were delivered through different modalities, targeted different lifestyle components, and were aimed at pregnant women with different vulnerabilities. The studies examining the effectiveness of the eHealth lifestyle interventions showed potential as some managed to significantly change health behaviors, but the results were inconsistent. This finding aligns with those of previous research on eHealth lifestyle interventions for the low-SEP population [58], showing that effect sizes are small and differ among the interventions. This enhances the need for insights into the experienced barriers and facilitators in the different phases of the development of an eHealth intervention. The insights gained from our review will be explored separately per phase.

For the design of eHealth lifestyle interventions, researchers were guided by stakeholders, existing health interventions, guidelines, and theoretical frameworks. Researchers that collaborated with their target group and other stakeholders (eg, health care professionals) often referred to these collaborations as facilitating to their intervention design [15,18-20,33,34,36-38,42,45]. While co-creating with the target group was mostly limited to content design of the intervention, other stakeholders were often involved in multiple phases of intervention development. Engaging in qualitative research with intended users can shape the foundational guiding principles of the intervention, and user feedback can help refine the content and functionality of the intervention [45]. Existing health interventions were examined to identify components that could be valuable for the design of new eHealth interventions [36,38,42,45,46,50,55,56]. For clinical content, researchers additionally consulted guidelines from organizations such as the American Congress of Obstetricians and Gynecologists [41]. In addition, various theoretical frameworks for behavior change were used to guide the content of the interventions. However, it often remained unclear why researchers chose the frameworks they used. Furthermore, even though frameworks could help guide health intervention design, evaluation, adaptation, and implementation, only some studies (4/29, 59%) mentioned using a framework to guide the development of their eHealth tool.

Various features and strategies were included in the eHealth lifestyle interventions to increase user engagement. First, including social components (eg, virtual peer support sessions and partner involvement) in the intervention was (expected to be) appreciated by end users [19,34-38,45,48,52]. Research indicates that pregnant women value partner involvement and support in eHealth lifestyle interventions and its benefits could extend beyond improving health outcomes [59]. Nevertheless, this review revealed a limited number of interventions (2/20, 10%) that actively involved partners to support maternal health behavior. Future eHealth lifestyle interventions should explore ways of engaging partners of vulnerable pregnant women as their involvement seems promising. Furthermore, although it was somewhat unclear whether tailoring led to better results in the included studies, tailoring the intervention to the characteristics and needs of the target group or individuals is expected to lead to increased engagement [60]. The same expectation applies to the use of incentives [61]. Technological support was provided to assist end users with any technological difficulty encountered. Although positive assumptions about pregnant women’s digital literacy due to their age may be made, one study highlighted an instance in which technical challenges adversely affected user engagement [19]. In addition, digital literacy has previously been identified as a barrier to mHealth adoption among people of a low socioeconomic status [62]. These results highlight the importance of addressing digital literacy when developing eHealth interventions for vulnerable pregnant women.

Recruitment of vulnerable groups for study purposes frequently presents challenges, as has been emphasized in a previous review [58] and was once more highlighted in this review, where most researchers encountered difficulties in the recruitment of vulnerable pregnant women for participation in their studies to test their eHealth lifestyle interventions. In a few studies (6/29, 21%), difficulties with recruitment resulted in a small sample size [20,34,39-41,48]. However, the researchers did not always explicitly discuss the barriers that kept them from achieving an adequate sample size. Interestingly, our findings show that recruitment barriers were not solely attributed to factors such as low patient volumes or lack of interest by participants. Health care providers, although often identified as facilitators in the recruitment process, were also identified as barriers, either because the research staff received limited support from health care providers or because recruitment was not feasible for health care providers operating in their natural setting [34,39]. Finally, resource aspects (eg, lack of access to internet and limited phone ownership) were mentioned as a barrier to the recruitment of vulnerable pregnant women [19,39,47].

The study by Mauriello et al [46] was the only one that not only met but also exceeded its recruitment goals within a short time frame, and they attributed this success to the willingness of the prenatal care staff and eagerness of the pregnant women attending the health center where they recruited from. However, it remains unclear what exactly caused this eagerness. Many studies (11/29, 38%) involved health care professionals in the recruitment process, whereas community-led recruitment was less prevalent. However, using a community-based participatory research approach has shown to be a promising strategy for conducting health disparity–related research in minority populations [63] and, therefore, might be considered in the design of future interventions targeting vulnerable pregnant women. In addition, including incentives could facilitate recruitment, although we only found one study that clearly showed that their reference to an incentive increased recruitment numbers [42,51].

Overall, to increase the successful recruitment of vulnerable pregnant women, researchers should consider all 4 conditions that were identified as barriers to recruitment in this review. First, it is important to identify the prevalence of the target group, and recruitment sites should be adjusted accordingly based on this prevalence. Second, researchers ought to incorporate methods to ensure that participation in eHealth interventions is appealing and minimally time-consuming for their target group. Including incentives could be particularly helpful in addressing this concern. Third, researchers should aim to either handle recruitment themselves or involve dedicated health care providers in the recruitment process, ensuring it does not become resource intensive or time-consuming for them or disrupt the delivery of health care. Finally, resource-related barriers to recruitment can be handled by enabling access to internet services during recruitment and providing the devices necessary for the use of the eHealth lifestyle intervention.

The insights from the facilitators and barriers encountered in previous eHealth implementation processes could help future researchers, health care professionals, and eHealth developers devise more effective strategies for forthcoming implementations. Nevertheless, most studies (18/29, 62%) failed to outline the implementation process of their eHealth lifestyle intervention. This could be attributed to the prevalence of pilot and design papers among the included studies, where the interventions had often not been implemented beyond these initial stages. However, a few facilitators could be identified related to implementation. First, collaboration with stakeholders was frequently emphasized as necessary for a successful implementation of the intervention [15,36,38,47,53]. Collaborating with stakeholders enhances the likelihood that interventions are designed and implemented in a manner that is relevant, acceptable, and feasible within real-world settings [64,65]. In addition, providing the intervention free of charge was important for uptake by end users. Furthermore, as eHealth lifestyle interventions often include the involvement of health care professionals, for whom time constraints can be a barrier to involvement, a low workload came forward as an important facilitator to eHealth implementation. In contrast, the costs associated with financial incentives and loaned devices were found to be a potential barrier to implementation.