A rapid review of digital health applications supporting self-care during pregnancy and postpartum period

Abstract

Background

With the growing adoption of mobile technologies, mHealth applications have emerged as valuable tools for enhancing maternal healthcare by providing accessible and flexible support throughout pregnancy and postpartum period. Despite their widespread use, limited evidence exists on how these apps are designed and evaluated for self-care, highlighting the need for a structured review.

Methods

A rapid review was conducted using a structured search in PubMed, Scopus, and Web of Science to identify studies focusing on digital applications delivered through mobile or web-based platforms that support self-care during pregnancy and postpartum. Eligible studies included randomized controlled trials, study protocols, and development papers published from 2015 to 2025. Data were extracted and narratively synthesized to examine app features, theoretical foundations, and evaluation strategies.

Results

A total of 13 studies were included, primarily evaluating mobile health (mHealth) interventions during pregnancy and postpartum period. The applications predominantly targeted mental health, lifestyle behaviors, and health literacy. Randomized controlled trials were the most common study design. Although several studies applied behavioral theories such as Cognitive Behavioral Therapy and Social Cognitive Theory, many lacked explicit theoretical frameworks.

Conclusions

mHealth applications show promise in supporting maternal self-care, particularly for postpartum mental health. However, many interventions lack theoretical grounding and long-term evidence. Future efforts should prioritize scalable, theory-based, and culturally tailored designs to enhance effectiveness and sustainability.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12884-026-09677-y.

Introduction

With the growing integration of technology into everyday life, mobile health (mHealth) applications have become influential tools in modern healthcare. These applications designed for smartphones and other mobile devices offer users convenient access to a wide range of health-related information, resources, and services, thereby improving patient engagement and health outcomes [1–3].

In recent years, the field of maternal health has particularly benefited from this digital transformation. As medical technologies related to pregnancy and childbirth have advanced, mobile apps and websites have increasingly entered this domain, offering innovative support for women during the prenatal and postnatal periods [4, 5]. Several widely used apps, such as What to Expect, Ovia Pregnancy, and Pregnancy+, provide features like personalized fetal development tracking, symptom monitoring, educational content, and virtual communities. Others like Flo, which evolved from a menstrual tracker to a comprehensive reproductive health tool, and Bonzun, which offers a virtual midwife interface, demonstrate the diverse capabilities of mobile platforms in supporting maternal well-being across different contexts [6].

One of the key advantages of mHealth apps is their flexibility. Unlike traditional, in-person care models, these applications can be accessed anytime and anywhere, which is especially valuable for pregnant women and new mothers who may face time constraints or logistical barriers. Home-based health behavior interventions delivered through mobile platforms are gaining popularity for precisely this reason they address many of the unique challenges mothers encounter in engaging with conventional programs [7, 8].

Moreover, mobile technology plays a vital role in promoting behavior change through information dissemination. During pregnancy and postpartum periods, women increasingly turn to digital media such as websites, blogs, discussion forums, and social media to seek health-related knowledge and peer support [9]. Among these, mobile apps stand out as particularly accessible and user-friendly tools. Studies show that over half of pregnant women use at least one app related to pregnancy, childbirth, or childcare [10, 11]. This widespread adoption reflects a global trend, as mobile health apps have become one of the most accessible sources of information and support for expectant mothers [12, 13]. According to a systematic review by Ameyaw et al. [14], the use of mHealth apps for maternal health care has increased globally, particularly in high-income countries, where these interventions have shown effectiveness in areas such as maternal anxiety and depression, gestational weight management, and behavioral modifications during pregnancy.

The growing reliance on digital health tools highlights their potential as scalable interventions to enhance maternal and fetal health outcomes worldwide.

Despite the increasing integration of mobile health (mHealth) technologies into maternal care, there remains a limited consolidated understanding of how these applications are conceptualized, developed, and evaluated particularly those aimed at promoting self-care during pregnancy and the postpartum period.

To address this gap, the present study conducts a rapid review to systematically identify and analyze existing digital applications (mobile or web-based) designed to support maternal self-care. The review focuses on the characteristics of these interventions including their goals, functionalities, theoretical foundations, and evaluation designs in order to extract critical insights that can inform the future design and development of an evidence-based, user-centered mHealth application tailored to the needs of women in this period.

Methods

A rapid review was undertaken to provide a timely synthesis of the existing literature about the design and development of a mobile application for maternal self-care due to the time-constrained nature of the development phase. This approach provided a streamlined yet systematic synthesis of evidence. Compared with a scoping review, which broadly maps available evidence, a rapid review is more focused on addressing specific research questions while applying time-efficient methods [15]. The review included both interventions that had already been developed and evaluated, as well as protocols and app design studies.

Search strategy

All searches were conducted between April 1 and May 20, 2025. A focused search strategy was employed to retrieve relevant literature on digital health applications (mobile or web‑based) for self-care in pregnancy and postpartum. Searches were conducted in PubMed, Scopus, and Web of Science using combinations of keywords and MeSH terms such as “Mobile Applications,” “Self-Care,” “Pregnancy,” and “Postpartum Period.” Boolean operators (e.g., “Pregnancy AND Mobile Applications”) were used to refine the search. In the initial search strategy, an additional concept related to engagement features (e.g., “gamification,” “interactive, or “behavioral intervention”) was included to identify applications incorporating behavior‑change or interactive components. This was intended to capture apps designed to actively support self-care behaviors. However, we acknowledge that such terminology may not be consistently used in the indexing or description of maternal self‑care applications. The search was limited to peer-reviewed articles published in English between January 2015 and May 2025. Studies specifically targeting women with chronic medical conditions (e.g., diabetes, hypertension) were excluded.

Truncation was not applied in the search strings, which may have limited retrieval of term variations. This was noted as a methodological limitation.

Inclusion and exclusion criteria

The eligibility criteria used to determine study inclusion and exclusion are presented in Table 1.

Table 1.

Eligibility criteria for primary studies

Category	Include	Exclude
Study designs	Randomized controlled trials (RCTs), non-randomized controlled trials, quasi-experimental studies with a comparator, protocol papers for interventions, and app design/development studies with described intended outcomes	Purely qualitative studies without an intervention; review articles
Populations	Pregnant or postpartum women (up to one year after childbirth); participants must be adult women (18+); subgroup data clearly extractable if mixed population; must be generally healthy (i.e., without specific medical conditions)	Women with specific medical conditions such as diabetes, hypertension, preeclampsia, or other chronic diseases; men, children, adolescents, non-pregnant populations
Interventions	Digital applications delivered through mobile apps or web‑based platforms designed to support self‑care, health promotion, or wellness during pregnancy or postpartum., with components such as: (1) Education (2) Behavior tracking (3) Reminders (4) Psychological support (5) Communication with providers Both mobile apps and web‑based platforms were included because many contemporary maternal self‑care interventions use cross‑platform digital formats.	Apps without user-facing interaction (e.g., passive monitoring only); SMS-only interventions or systems without interactive user interfaces
Outcomes	Behavioral outcomes (e.g., self-care, physical activity, diet, breastfeeding), psychological outcomes (e.g., depression, anxiety, self-efficacy), maternal health indicators (e.g., weight gain, sleep)	Technology performance outcomes (e.g., usability errors, bugs); provider/system-level outcomes only
Timing/setting	Any country or setting; studies published from 2015 onward	Studies published before 2015
Language	English	All other languages

The table outlines the inclusion and exclusion criteria applied across study design, population, intervention type, outcomes, timing, setting, and language

Screening and selection process

Titles and abstracts retrieved from the database searches were screened for relevance based on the inclusion and exclusion criteria. Initial screening was conducted by one reviewer. Full texts of potentially relevant articles were then assessed independently by two reviewers to confirm eligibility. Disagreements were resolved through discussion and consensus. Reasons for exclusion at the full-text stage were documented.

Owing to the rapid review methodology, title and abstract screening was initially performed by one reviewer. To mitigate the potential risk of selection bias, all potentially eligible full-text articles were independently assessed by two reviewers, and any disagreements were resolved through discussion until consensus was reached.

Data extraction

A structured data extraction form was developed in Microsoft Excel and piloted prior to use. The form was initially tested on a small subset of included studies to assess clarity, completeness, and consistency of the predefined extraction fields. Based on this pilot phase, minor refinements were made to variable definitions and data categories before full data extraction was undertaken. Data extraction was primarily conducted by the first author using a standardized extraction form. To minimize potential extraction errors and reviewer bias, extracted data were independently checked by a second reviewer, and any uncertainties or discrepancies were resolved through discussion with the research team.

Extracted data included:

• Article information: title, authors, publication year, country.

• Intervention characteristics: aim, duration, tool type (e.g., mobile app or web‑based application), theoretical model used.

• Study design: type of study conducted.

Where available, additional information such as population characteristics, application features, and evaluation outcomes (e.g., usability, user satisfaction, engagement) was also recorded.

Data extraction was carried out by one reviewer and independently verified by a second. While this approach aligns with rapid review methodology, the possibility of bias due to single extraction cannot be fully excluded; therefore, discrepancies and uncertainties were resolved in consultation with the supervisory team.

Quality assessment

The methodological quality of the included studies was assessed using the JBI Critical Appraisal Checklist for Randomized Controlled Trials [16]. Although the same JBI tool was applied across different RCT designs, this approach is consistent with rapid review guidance where methodological streamlining is recommended. One methodological study aimed at developing and validating a mobile application was identified but not included in the quality appraisal table, as it did not report intervention outcomes. The results of the methodological quality assessment are presented in Table 2.

Table 2.

JBI critical appraisal checklist for Randomized Controlled Trials (RCTs)

Study (Author, Year)	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Overall appraisal (JBI)	Comments
Kamarudin et al., 2025 [17]	Yes	Yes	Yes	Unclear	Yes	Yes	Unclear	Yes	Yes	Unclear	Yes	Yes	Yes	Include	Most methodological criteria were met; however, lack of blinding may introduce performance and detection bias.
Osman et al., 2025 [18]	Yes	Yes	Yes	No	No	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes	Include	Well-designed RCT with appropriate randomization and allocation concealment, but open-label design and per-protocol analysis may introduce performance and attrition bias.
Van Uytsel et al., 2023 [19]	Yes	Yes	Unclear	No	No	Yes	No	Yes	Yes	Unclear	Yes	Yes	Yes	Include	Study protocol only; therefore, baseline comparability and follow-up outcomes could not be assessed. Randomization and allocation concealment were adequately described. The trial is open-label without blinding of participants, care providers, or outcome assessors. Intention-to-treat analysis was prespecified.
Y. Lee et al., 2023 [20]	Yes	Yes	Yes	No	No	Unclear	Yes	Yes	Unclear	Yes	Yes	Yes	Yes	Include	Randomized, multicenter RCT with robust randomization and validated outcome measures. Lack of blinding due to intervention nature; ITT not clearly reported.
Koeryaman et al., 2023 [21]	No	No	No	Yes	Unclear	Yes	Unclear	Yes	Yes	Yes	No	Yes	Yes	Include	App-based randomized repeated-measures study with automated allocation and participant blinding. However, allocation was quasi-random (alternating order) and ITT analysis was not applied.
Tan et al., 2022 [22]	Yes	Unclear	Yes	No	No	Yes	Unclear	Yes	Yes	Yes	Yes	Yes	Yes	Include	Overall, the study was well-designed with appropriate randomization and analysis; however, allocation concealment and blinding were not clearly described, which may introduce a potential risk of bias.
Brunelli et al., 2022 [23]	Yes	Unclear	Yes	No	No	Yes	Yes	Yes	Yes	Yes	Unclear	Yes	Yes	Include	The study followed CONSORT cRCT standards with reliable outcome measures and appropriate analysis. However, allocation concealment and ITT analysis were not reported, which may introduce some bias.
Farzi Karamolahi et al., 2021 [24]	Yes	Yes	Yes	No	No	Yes	Unclear	Yes	Yes	Yes	Yes	Yes	Yes	Include	The trial used proper block randomization and concealed allocation with valid measurement tools and appropriate analyses. However, blinding of participants and assessors was not reported, which could introduce some detection bias.
Sandborg et al., 2021 [25]	Yes	Yes	Yes	No	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Include	Appropriate randomization and validated outcome measures were reported. Blinding was not feasible because of the intervention design, and intention-to-treat analysis was not clearly reported.
Nuruddin et al., 2021 [26]	Yes	Yes	Unclear	No	No	Yes	Unclear	Yes	Yes	Unclear	Yes	Yes	Yes	Include	Randomization and allocation concealment were adequate, and validated tools with intention-to-treat analysis were planned. However, participant and provider blinding was not possible, and assessor blinding was unclear.
Evans et al., 2019 [27]	Yes	Yes	Yes	No	No	Yes	Unclear	Yes	Yes	Yes	Yes	Yes	Yes	Include	The trial applied proper randomization, allocation concealment, validated outcome measures, and ITT analysis. Blinding of participants and providers was not possible, and assessor blinding was unclear.
Choi et al., 2016 [28]	Yes	Yes	Yes	No	No	Yes	Unclear	Yes	Yes	Yes	Yes	Yes	Yes	Include	Proper randomization and allocation concealment were applied, outcomes measured with validated tools, and ITT analysis used. Participant and provider blinding was not possible, and assessor blinding was unclear.

The table summarizes the appraisal results for each included randomized controlled trial across the 13 JBI checklist items, along with overall quality judgments and brief methodological comments

Data synthesis

A narrative synthesis approach was used to analyze and present the findings. The synthesis focused on identifying and organizing:

• Core features and functions of mobile apps designed for self-care in pregnancy/postpartum.

• Common design approaches, user interface strategies, and content areas.

• Challenges and gaps in development, evaluation, and reporting.

The results were categorized thematically to inform best practices and future app development standards in the field of maternal self-care.

Results

The process of searching and selecting studies is depicted in Fig. 1.

Fig. 1.

PRISMA 2020 Flow Diagram – Narrative Description. The diagram illustrates the identification, screening, eligibility assessment, and final inclusion of studies in the rapid review of mHealth applications supporting self-care during pregnancy and the postpartum period. Symbols follow PRISMA 2020 conventions; asterisks indicate manually removed duplicates or records excluded for non‑eligibility reasons

Intervention characteristics

A total of 13 studies were included, focusing on digital health interventions during pregnancy and the postpartum period. The majority of interventions (n = 12) were delivered through mobile applications, while one study utilized a web‑based platform. Some interventions were implemented as standalone digital tools, whereas others were combined with additional components such as social media platforms (e.g., Facebook) or coaching sessions (Table 2). Nine records could not be retrieved because the full texts were not accessible through institutional subscriptions or publicly available sources. Several of the included trials were conducted as pilot or preliminary studies aimed at evaluating feasibility and initial effectiveness. All descriptions of intervention features were directly extracted from the included studies (Table 3), and citations have been added accordingly.

Table 3.

Characteristics of retrieved papers

Author (year)	Intervention Aim	Intervention Period	Tool Type	Study Design	Reported theoretical framework
Kamarudin et al., 2025 [17]	To evaluate the effectiveness of the LoVE4MUM app in preventing postpartum depression	Postpartum	Mobile app	RCT	Cognitive Behavioral Therapy (CBT) and Psychoeducation
Osman et al., 2025 [18]	To evaluate the effectiveness of the Smart Mama app on postpartum depression, anxiety, and maternal-infant bonding	Postpartum	Mobile app	RCT	Roy Adaptation Model (RAM)
Van Uytsel et al., 2023 [19]	To assess the effect of a postpartum lifestyle intervention (INTER-ACT) on mental health outcomes (depression, anxiety, coherence, QoL)	Postpartum (up to 6 months)	Mobile app + coaching sessions	RCT	No explicit theory; Behavior Change Techniques (motivational interviewing, goal setting, action planning, self-monitoring)
Y. Lee et al., 2023 [20]	To evaluate the effectiveness of a mobile-based intervention—Self‐care for Pregnant Women at Work (SPWW)—in enhancing the self‐care practices of pregnant women who work.	Pregnancy	Mobile app	RCT	None reported
Koeryaman et al., 2023 [21]	To examine the effectiveness of the SISFORNUTRIMIL web-based app in improving dietary diversity, nutrient intake, and healthy weight gain in pregnant women	Pregnancy	Web-based application	RCT	Self-Monitoring Theory
Souza FM et al., 2022 [29]	To develop and validate the content of a mobile app to support pregnant women during prenatal care	Pregnancy (prenatal period)	Mobile app	App development and content validation study	Contextualized Instructional Design (CID) Model (Instructional Design Framework)
Tan et al., 2022 [22]	Prevent perinatal depression and reduce dropout rate	From 12–16 weeks of pregnancy to 6 months postpartum (with 12-month follow-up)	Mobile app	Cluster RCT Protocol	No explicit behavioral theory; intervention based on Cognitive Behavioral Therapy (CBT) with dynamic personalized and stratified intervention strategy
Brunelli et al., 2022 [23]	To evaluate the effectiveness of a mHealth app in supporting women during the first 1000 days (pregnancy to 24 months) and promoting preventive health behaviors (e.g., vaccination, smoking cessation, weight gain control)	Conception to 24 months	Mobile app	RCT	None reported
Farzi Karamolahi et al., 2021 [24]	Health literacy	Pregnancy	Mobile app	RCT	None reported
Sandborg et al., 2021 [25]	Promote healthy gestational weight gain, improve diet, and increase physical activity during pregnancy	Pregnancy	Mobile app	RCT	Social Cognitive Theory (SCT)
Nuruddin et al., 2021 [26]	To assess the efficacy of an mHealth program in improving diet, supplement use, and physical activity during pregnancy	Pregnancy	Mobile app	RCT	The theory of planned behavior, the theory of self-efficacy, the Fogg behavior model, and the transtheoretical model for behavior change
Evans et al., 2019 [27]	Weight management	Postpartum	Mobile app + social media (Facebook group)	Small RCT	None reported
Choi et al., 2016 [28]	To test the feasibility and potential efficacy of an mHealth intervention to promote physical activity in inactive pregnant women	Pregnancy	Mobile app + daily messages + activity diary	Randomized pilot study	Social Cognitive Theory (SCT)

The table presents key characteristics of the retrieved studies, including intervention aims, intervention period, tool type, study design, and underlying theoretical models

Targeted outcomes and populations

Most interventions aimed to improve mental health outcomes, particularly the prevention or management of postpartum depression, perinatal depression, or maternal selfcare. Others addressed dietary behaviors, physical activity, gestational weight gain, or health literacy. The timing of interventions varied, with many starting during pregnancy and continuing into the postpartum period, while others focused exclusively on the postpartum phase.

Study designs

The 13 included studies comprised different research designs. Most were completed intervention trials (n = 11), including standard randomized controlled trials, a small RCT, and a randomized pilot study. In addition, one paper was a protocol for a planned cluster randomized controlled trial, and one was a methodological study describing the development and content validation of a mobile application. The protocol and the development study were included to illustrate emerging intervention designs and app characteristics but were not considered as evidence of intervention effectiveness.

Theoretical foundations

Only a minority of studies explicitly reported a guiding theoretical framework. When stated by the study authors, the most frequently mentioned frameworks included Cognitive Behavioral Therapy (CBT), Social Cognitive Theory, and the Transtheoretical Model. However, several studies did not explicitly report a theoretical basis for the intervention design. Therefore, only explicitly reported theories were considered when summarizing the theoretical grounding of the interventions.

Domain-specific interventions patterns

The included interventions could be broadly categorized into three main domains: mental health, lifestyle modification, and health literacy/self-care education.

Five interventions primarily targeted maternal mental health during pregnancy or the postpartum period [17, 22, 27]. Although the specific intervention strategies differed, these applications generally combined educational content with psychological support components such as psychoeducation, relaxation exercises, emotional regulation, self-monitoring, and motivational messages. Some interventions additionally incorporated professional consultation or lifestyle coaching, whereas others were designed as entirely self-guided mobile applications [17–19, 22].

Another five interventions focused on promoting healthy lifestyle behaviors during pregnancy, particularly nutrition, physical activity, gestational weight gain, and dietary self-management [21, 25, 26, 28]. Across these studies, the most frequently reported intervention components included food or activity tracking, goal setting, personalized feedback, educational materials, and progress monitoring. Most of these interventions were delivered through mobile applications, while one study employed a web-based platform to facilitate dietary monitoring and nutritional counseling [21].

Three interventions primarily addressed maternal health literacy and self-care education [4, 20, 24]. These applications mainly provided structured educational resources covering topics such as prenatal care, nutrition, physical activity, stress management, breastfeeding, newborn care, appointment reminders, and communication with healthcare professionals. Compared with mental health and lifestyle interventions, these studies placed greater emphasis on information delivery and educational support rather than intensive behavior-change strategies.

Across all included studies, mobile applications were the predominant delivery platform (12 of 13 studies), with only one intervention using a web-based application [21]. Self-monitoring was the most frequently implemented feature (11 studies), followed by reminders or notifications (9 studies) and feedback or progress reports (6 studies). Direct or indirect communication with healthcare professionals was comparatively uncommon, being reported in only three interventions. Overall, the reviewed applications most commonly targeted postpartum mental health (n = 5) and healthy lifestyle behaviors during pregnancy (n = 5), whereas relatively few focused primarily on maternal health literacy or comprehensive mother–infant care (Table 4).

Table 4.

Functional features and targeted health domains of the included applications

Feature / Functionality	N of Studies (N = 13)	Examples
Self-monitoring (logs, diaries, tracking)	11	INTER-ACT, SISFORNUTRIMIL, HealthyMoms, SPWW, Fitbit-based interventions
Reminders / Notifications	9	LoVE4MUM, Smart Mama, Healthy Pregnancy (Gestação Saudável)
Feedback & Progress Reports	6	INTER-ACT, HealthyMoms, BeFAB, SISFORNUTRIMIL
Direct/Indirect Professional Support	3	Gestação Saudável, Smart Mama, INTER-ACT
Mental Health Focus (e.g., PPD, anxiety)	5	LoVE4MUM, Smart Mama, INTER-ACT, Tan et al., Evans et al.
Nutrition / Dietary Behavior	5	SISFORNUTRIMIL, HealthyMoms, Nuruddin et al., Brunelli et al., Choi et al.
Physical Activity Promotion	4	Fitbit, HealthyMoms, Choi et al., BeFAB
Health Literacy / Education	2	SPWW, Karamolahi et al.

The table summarizes the most frequently reported app features, the number of studies in which each feature was identified, and representative examples

Discussion

This rapid review included studies that used digital applications delivered through mobile or web‑based platforms as the primary intervention tool. These findings indicate the growing use of digital technologies in maternal health as multifunctional tools to support various aspects of self-care and health promotion.

Although this review identified several completed intervention studies evaluating digital applications for maternal self‑care, two included papers did not report effectiveness outcomes: one was a study protocol and another described the development and validation of an application. Additionally, Brunelli et al. [23] was retained because, despite addressing a broader age window, the intervention met the core definition of a maternal self‑care digital tool. These studies were included to provide insight into emerging intervention designs but were not considered as evidence of effectiveness.

The majority of studies employed randomized controlled trial (RCT) designs, reflecting an acceptable level of scientific rigor in assessing intervention effectiveness. However, many of these trials were conducted on a pilot or experimental scale, highlighting the need for larger, multicenter studies. A notable variation was observed in the type, duration, and structure of the interventions. Some apps, such as LoVE4MUM [17] and Smart Mama [18], were specifically focused on mental health, while others like HealthyMoms [25] and BeFAB [27] adopted more multidimensional approaches.

Across studies, interventions most frequently combined education, self‑monitoring, and reminder functions, whereas behavior‑change mechanisms such as goal‑setting, feedback loops, or social support were inconsistently applied. This pattern may partly explain the mixed outcomes reported in the included trials and reflects the early developmental stage of mHealth interventions for maternal self‑care.

Although a few interventions were grounded in behavioral theories such as Cognitive Behavioral Therapy (CBT) [17, 22], Social Cognitive Theory (SCT), or the Transtheoretical Model (TTM), in several studies, the intervention design or the application itself was not explicitly grounded in a behavioral theory. This is a significant limitation, as interventions designed based on theory tend to be more effective and allow for greater replicability and generalizability. Concerningly, many mHealth apps are not based on any behavior change theory, and in many cases, their effectiveness has not been adequately evaluated [30, 31]. This is particularly relevant considering the growing recognition of mobile apps’ potential to induce behavior change in digital health interventions [32]. Without a solid theoretical foundation, the long-term and sustained impact of behavior change promoted by these applications remains questionable.

Various features were employed to enhance user engagement, including reminders, notifications, progress dashboards, self-monitoring tools, gamification elements, and opportunities for communication with counselors or support groups. Apps that incorporated multimedia content, interactivity, and professional consultation were more likely to achieve higher levels of user interaction and effectiveness.

Cultural and geographic diversity was considered in some interventions for example, BeFAB [27] targeted African American women, and PurUmeed Aaghaz [26] was developed in an Asian context. This highlights the importance of culturally tailored content, as adapting the language, format, and content of mHealth interventions to local norms and conditions can positively affect their uptake and effectiveness. If an mHealth app lacks contextual or cultural alignment with its target audience, there is a risk of low adoption or misinterpretation. For instance, an app designed for prenatal care in European countries may not be effectively translated to sub-Saharan African contexts due to differences in language, beliefs, and healthcare infrastructure.

Despite notable progress in the field of maternal digital health, several important research and practical gaps persist. Many studies lack a clearly defined theoretical framework to guide intervention design and interpretation of outcomes. There is also limited evidence on the long-term sustainability of intervention effects and the persistence of behavioral changes over time. Furthermore, few studies have comprehensively evaluated how various app components interact to influence maternal health outcomes. Another key challenge lies in sustaining user engagement throughout the intervention period, which remains a critical factor for achieving meaningful and lasting health improvements.

Based on the findings of this review, several implications can be drawn for both future research and practical application. Future studies should employ well-defined theoretical frameworks to guide the design and implementation of interventions, ensuring that the underlying behavioral mechanisms are clearly articulated. Incorporating interactive, multimedia, and personalized consultation features may help enhance user engagement and adherence, although these aspects were not directly evaluated in the present review. Moreover, interventions should be tailored to the local cultural, linguistic, and social contexts to improve relevance and accessibility for target populations. Longitudinal evaluations are also needed to assess the long-term sustainability of health behaviors and outcomes during the postpartum period. Finally, future research should include cost-effectiveness analyses to determine the feasibility of integrating such digital health interventions into national maternal health programs.

Limitations

A rapid review approach was adopted to provide timely evidence to inform the design of a maternal self-care mobile application within the constraints of the project timeline and available resources.

This review has several limitations. First, methodological adaptations inherent to rapid reviews including time-limited searching, single-reviewer title and abstract screening, primary data extraction by one reviewer with independent verification by a second reviewer, and exclusion of grey literature may have reduced the comprehensiveness of the evidence and introduced selection or extraction bias compared with a full systematic review. However, full-text screening was conducted independently by two reviewers, and discrepancies were resolved through discussion. Restricting the review to English-language publications may also have introduced language bias.

Second, the review was limited to interactive mobile or web-based applications, excluding SMS-only interventions and passive monitoring systems, which may have omitted relevant evidence from other digital health modalities. In addition, the search strategy included engagement-related terms (e.g., gamification, interactive, and behavioral intervention), which may have unintentionally reduced retrieval because such terminology is not consistently used in maternal health app studies.

Third, only studies involving generally healthy pregnant or postpartum women were included, limiting the generalizability of the findings to women with specific medical conditions. Finally, limited access to some commercial or proprietary applications may have prevented comprehensive assessment of all relevant intervention features, and end users were not directly involved in shaping the review questions or interpreting the findings.

Future research should include multilingual databases, broader maternal populations, greater end-user involvement, and longitudinal implementation studies to better evaluate the real-world effectiveness and sustainability of maternal mHealth interventions.

Conclusion

This rapid review provides an overview of the growing role of mobile health (mHealth) applications as potentially valuable tools for supporting maternal self-care, with a predominant focus on mental health during the postpartum period. The findings highlight both the potential and limitations of current app-based interventions. While several studies demonstrated acceptable methodological rigor, often using randomized controlled designs, many were pilot studies with limited evidence regarding scalability and sustainability. Furthermore, the absence of clearly defined theoretical frameworks in many interventions raises concerns about their effectiveness and replicability.

Engagement features such as gamification, multimedia content, and access to professional support were associated with increased user interaction, suggesting that design elements may play an important role in user engagement and potentially in the success of digital interventions. Additionally, culturally tailored apps appeared more likely to be accepted by diverse populations, emphasizing the importance of contextual relevance in mHealth design.

Despite encouraging trends, significant research gaps remain, including limited long-term outcome data, insufficient exploration of how specific app components influence behavior change, and challenges in sustaining user engagement. While several digital interventions have been developed to support maternal self-care during pregnancy and the postpartum period, the number of rigorously evaluated interventions remains limited, highlighting the need for further high-quality randomized controlled trials.

Overall, this rapid review primarily provides a descriptive overview of the current landscape of mHealth applications for maternal self-care and aims to inform future intervention design. Although these tools show potential, stronger and more consistent evidence will be needed to determine their effectiveness and long-term impact on maternal health outcomes.

Authors’ contributions

YJ and ESh conceptualized and designed the study. YJ, HS, and RR conducted the literature search, screening, and data extraction. YJ performed the data analysis and synthesis. YJ drafted the first version of the manuscript. ESh, RR, HS, and SHH and RRZ critically reviewed and revised the manuscript for important intellectual content. All authors read and approved the final manuscript.

Funding

This study was supported by a research grant from the Deputy of Research and Technology, Tehran University of Medical Sciences (TUMS), Iran.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Declarations

Ethics approval and consent to participate

Ethical approval was obtained from the Research Ethics Committee of Tehran University of Medical Sciences (Approval Code: IR.TUMS.SPH.REC.1403.289).

Competing interests

The authors declare no competing interests.

Consent for publication

Not applicable.