mHealth apps for maternal mental well-being among pregnant and postpartum women: a systematic review

Introduction

Background

Pregnancy is a period of significant physiological and psychological changes for women (1,2). Although physical health is usually prioritized during pregnancy, mental well-being is often overlooked, increasing the risk of serious mental health problems later (3,4). The World Health Organization (WHO) reports that approximately 10% of women worldwide experience mental health problems, primarily depression, during pregnancy, with rates increasing to 13% postpartum (5). These percentages are even higher in low- and middle-income countries (LMIC) (4), reaching 15.6% during pregnancy and 19.8% after delivery (5). These mental health challenges can have long-term effects on both mother and child, including perinatal depression and impaired mother-infant attachment (2). Children born to mothers with poor mental health can also experience adverse outcomes, including low birth weight and delayed cognitive development (6).

Rationale and knowledge gap

Mobile health (mHealth) solutions offer a promising approach to address maternal mental well-being during and after pregnancy (2,3,7-13). These smartphone-based interventions can provide accessible and low-cost support through techniques such as breathing exercises and mindfulness-based meditation (9). Although a growing number of studies have examined mHealth applications (apps) in this context, the evidence remains fragmented. Existing work varies in quality, focuses on narrow populations, and often lacks consistent outcome measures or long-term follow-up. In addition, critical aspects such as user participation, adherence strategies, and sustained effectiveness are underreported, making it unclear which characteristics of the evaluated interventions truly contribute to maternal well-being.

Therefore, a systematic review of existing apps is needed to consolidate current knowledge, compare intervention strategies, and identify effective practices and persistent gaps. This review provides a structured framework to guide future mHealth app development, evaluation, and clinical integration in mental healthcare for pregnant and postpartum women.

Objective

The objective of this review is to examine and synthesize existing evidence on mHealth apps designed to support maternal mental well-being during pregnancy and the postpartum period. Our aim is to clarify the scope of available interventions, evaluate their reported effectiveness, and highlight critical gaps that must be addressed to advance the design, evaluation, and implementation of mHealth solutions for maternal mental health. We formulate the following questions to guide our research.

RQ1: what mHealth apps have been developed and tested to support mental well-being in women during and after pregnancy, and what are their origin and platform characteristics?

• Rationale: understanding the current landscape of existing mHealth apps is required to identify trends, gaps, and potential areas of improvement necessary to address mental well-being in women during pregnancy.

RQ2: what mental health issues do the mHealth apps diagnose or address?

• Rationale: identifying the types of mental health problems addressed by the apps clarifies the premise of the study and the design of the intervention.

RQ3: what types of questionnaires have been integrated into mHealth apps to detect mental health issues?

• Rationale: identifying the types of questionnaires to screen for mental health issues helps to evaluate their reliability and validity, ensuring that the apps can accurately identify mental health problems in pregnant women.

RQ4: what types of psychological interventions [e.g., cognitive behavioral therapy (CBT), mindfulness, mood tracking] are incorporated into mHealth interventions targeting perinatal and postpartum mental health?

• Rationale: identifying the intervention approaches used in these apps helps to clarify the scope of strategies that mHealth can support to promote maternal mental well-being. Furthermore, evaluating these approaches in relation to the reported outcomes helps identify which methods are most effective in improving the mental health of pregnant women.

RQ5: what specific features of mHealth apps have been developed to improve mental well-being outcomes during and after pregnancy?

• Rationale: analyzing specific features that helps guide the development and enhancement of apps to better support maternal mental health.

RQ6: how did users rate the usability, satisfaction, and engagement of the reviewed mHealth apps?

• Rationale: understanding how pregnant and postpartum women experienced and responded to mHealth tools is essential for assessing their real-world acceptability.

RQ7: how effective are mHealth apps in improving maternal mental health compared to control groups and alternative interventions?

• Rationale: this is essential for assessing the value and feasibility of mHealth apps in maternal mental health.

RQ8: what limitations and research gaps exist in the current literature on mHealth interventions for maternal mental health?

• Rationale: identifying such areas would provide guidance to researchers and developers to advance the field and enhance mHealth effectiveness for the target population.

We present this article in accordance with the PRISMA reporting checklist (14) (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-2025-72/rc).

Methods

We conducted a systematic literature review to identify and evaluate mHealth apps designed to support maternal mental well-being during the perinatal and postpartum periods. The databases used to locate relevant studies were Scopus and PubMed, selected due to their comprehensive coverage of the literature on healthcare and information technology and their established use in systematic reviews on related topics (15). The search was conducted on August 17, 2025. We limited our search to articles written in English. The initial screening of articles was conducted based on title and abstract, followed by full-text reviews to confirm eligibility. The initial title and abstract screening were conducted independently by three reviewers using Zotero reference management software. Inter-reviewer agreement was 84.2% (16 out of 19 agreements) at the abstract screening stage and 85% (17 out of 20 agreements) at full-text screening. Disagreements were resolved through discussion. The PRISMA flow diagram of our approach is summarized in Figure 1.

Figure 1 PRISMA flowchart for the literature search process. mHealth, mobile health.

Search strategies

The search strategy was developed using a combination of keywords and Boolean operators. This initial search yielded 2,127 potentially relevant articles. No lower date limit was applied to the search, as mHealth research targeting maternal mental well-being is a relatively emerging field and imposing a cutoff would risk excluding foundational studies from its early development. The exact search string that was used to find the articles is given below: (“pregnan*” OR “matern*” OR “mother*” OR “antenatal” OR “perinatal” OR “peripartum” OR “postpartum” OR “post-partum” OR “postnatal” OR “childbearing”) AND (“mental” OR “psychological” OR “emotional” OR “mindful” OR “wellbeing” OR “well-being” OR “wellness” OR “depression” OR “postpartum blue*” OR “baby blue*” OR “anxiety” OR “stress” OR “distress*”) AND (“mHealth” OR “mobile health” OR “digital health” OR “eHealth” OR “telehealth” OR “telemedicine” OR “mobile app*” OR “smartphone” OR “app” OR “mobile technolog*” OR “mobile intervention*”).

Inclusion and exclusion criteria

After removing duplicates, the titles and abstracts were screened for relevance. The full text review of the remaining articles was then conducted using the following inclusion criteria: (I) original research; (II) focused on mHealth apps and maternal mental well-being during or after pregnancy; and (III) included a diagnostic or intervention component for mental health problems. Fifteen articles met these criteria and were included in the final review.

Studies were excluded if they were: (I) review articles, systematic reviews, or meta-analyses; (II) short papers, conference abstracts, or opinion pieces without original empirical data; (III) study protocols or qualitative studies; (IV) not focused on mHealth apps specifically; (V) not related to maternal mental well-being during or after pregnancy; or (VI) not including a diagnostic or intervention component for mental health problems. Articles written in other languages than English were also excluded.

Data extraction

Following the full-text eligibility screening, data were systematically extracted from each of the 15 included studies by two independent reviewers using a standardized extraction form. Discrepancies were resolved through discussion and, where necessary, arbitration by a third reviewer. Extraction focused on three domains: publication profile, research objectives, and mHealth intervention characteristics (Figure 2).

Figure 2 Data extraction themes used to categorize the extracted data.

Publication profile

• First author location: identified countries of origin of the first authors researching pregnant and postpartum women’s mental health;
• Publication type: classified as a journal or conference article;
• Publication year: year of publication.

Research objectives

• Diagnosing mental health issues: examined the diagnostic capabilities of mHealth interventions, including the use of validated questionnaires;
• Addressing mental health issues: targeted specific mental health concerns such as depression, anxiety, and stress;
• Improving mental well-being: explored strategies designed to enhance overall maternal mental well-being;
• Evaluating performance and feasibility: assessed intervention performance and feasibility using standardized scales and measures.

mHealth or mobile app

• Platform: type of device/intervention format (Android, iOS, or web-based);
• App period: targeted timeframe of intervention (perinatal, postpartum, or both).

Results

Literature search outcomes and characteristics of included studies

Our systematic search identified 2,127 articles from PubMed and Scopus. After removing duplicates, the remaining records were screened for title and abstract, followed by full-text review against the inclusion criteria. Five studies met most but not all inclusion criteria and warrant specific mention. Three were study protocols without empirical outcome data, each describing planned mHealth interventions for perinatal mental well-being that had not yet reported results at the time of our search; two further studies were excluded because they employed purely qualitative designs without a diagnostic or intervention component meeting our inclusion criteria. This process resulted in the final selection of 15 articles for in-depth analysis. The included studies were published between 2017 and 2024. Two articles were part of the same research project (16,17). The search flow and the screening process are illustrated in Figure 1. Most of the included articles, 13 in particular, were published in journals, while 2 articles were conference papers. Geographically, the studies originated mainly from Europe (n=8). Italy and the United States each contributed three articles, representing the largest national outputs.

No formal assessment of risk of bias was conducted for individual studies, as no validated quality appraisal tool was applied during the review process. No meta-analysis was performed, as the substantial heterogeneity in study designs, target populations, intervention types, and outcome measures across the 15 included studies precluded meaningful statistical pooling. Results are therefore presented as a narrative synthesis structured around the eight pre-specified research questions. Given that the majority of included studies were pilot or feasibility trials with small sample sizes, that only one completed randomized controlled trial (RCT) was identified, and that no formal risk-of-bias assessment was undertaken, the overall certainty of the evidence is considered low. Findings should be interpreted as preliminary indicators of potential benefit rather than as conclusive proof of effectiveness.

Addressing RQs

RQ1: what mHealth apps have been developed and tested to support mental well-being in women during and after pregnancy, and what are their origin and platform characteristics?

Our review identified 13 distinct mHealth apps across the included studies. Table 1 lists the mHealth apps found from the reviewed articles. These included both research-specific (n=9) and pre-existing commercial apps (n=4), available as web-based, Android, or iOS versions. For example, “SerenaMente Mamma” (9), a research-specific app, offers mindfulness-based interventions for prenatal mental well-being on both Android and iOS. “Headspace” (12), a commercial app, provides pregnancy support across web, Android, and iOS platforms. In addition to the 13 apps identified, our review also included a prospective cohort study using the Patient-informiertinteraktiv-Arzt (PiiA), meaning “patient-informed interactive doctor” in German, online platform (18).

Lorenzen et al. (22) used a web-based intervention to provide both prenatal and postpartum support. However, participants mentioned that a smartphone-based app would be much easier to access. Within the Obesity-Related Behavioral Intervention Trials model, the “TreC Mamma” app delivers mindfulness-based materials in various formats through a virtual coach named Maia (23). Progga et al. (25) explored a range of mHealth tools, including smartphone apps, web platforms, social media groups, and online support communities used by pregnant and postpartum women for perinatal mental health support. Finally, Kamarudin et al. (24) used “LoVE4MUM”, a bilingual (English and Malay) mobile app that provides five self-guided modules based on postpartum depression, coping strategies, CBT-based tools, emotional regulation, and help-seeking skills on Android and iOS.

RQ2: which mental health issues do the mHealth apps diagnose or address?

The reviewed mHealth apps primarily addressed anxiety (n=12), depression (n=8), and stress (n=4). Some studies, such as Sarhaddi et al. (19), also examined underlying issues such as loneliness. Notably, Branjerdporn et al. (11) and Rizzi et al. (23) addressed all three mental well-being issues in their work. Kamarudin et al. (24) focused on enhancing postpartum depression literacy and reducing negative automatic thoughts through “LoVE4MUM”. Progga et al. (25) investigated technologies aimed at combating misinformation around perinatal mental health, while also supporting users coping with depression, anxiety (including postpartum depression), stress, and post-traumatic stress disorder (PTSD) symptoms. Table 2 summarizes the mental well-being issues targeted by each reviewed article.

RQ3: what types of questionnaires have been integrated into mHealth apps for screening mental health issues?

The detection of mental health problems is crucial for pregnant and postpartum mothers, as it enables the early detection and management of mental health concerns. Table 3 summarizes the screening tools used in the reviewed articles. The Edinburgh Postnatal Depression Scale (EPDS) was the most commonly employed, appearing in 11 of the 15 included studies (6,7,9,11,12,16-18,21,24,25) to identify signs of depression or anxiety during or after childbirth. EPDS scores range from 0 to 30, with scores that are 13 indicating an 80% likelihood of depression (26). Several authors, including Branjerdporn et al. (11), recommended referring women with scores above this threshold for further psychiatric evaluation.

While several other tools were identified in our review, none were used as frequently as EPDS. Among tools appearing in more than one study, the Perceived Stress Scale (PSS) was used in two studies (6,12), the Generalized Anxiety Disorder 7-item (GAD-7) was used in two studies (6,20), the Positive and Negative Affect Schedule (PANAS) was used in two studies (12,23), the 12-item Short Form Health Survey (SF-12) was used in two studies (6,23), and the Depression, Anxiety, and Stress Scale-21 (DASS-21) was used in two studies (21,23). The Short Form State Trait Anxiety Inventory 6-item (sSTAI-6), a survey used to assess perinatal anxiety symptoms, appeared in one study (18). The Whooley Questionnaire (WQ), a two-item screener often followed by the EPDS, if concerning signs are detected, was used in one study (7). Several tools appeared in only a single study, reflecting the breadth of assessment approaches across the included articles. These included the Impact Event Scale-Revised (IES-R), a 22-item self-report questionnaire rated on a 5-point Likert scale (23), and the Five Facet Mindfulness Questionnaire (FFMQ) and Emotion Regulation Questionnaire (ERQ), both used in the context of a mindfulness-based intervention (23). Kamarudin et al. (24) additionally used the Postpartum Depression Literacy Scale (PoDLiS) and the Automatic Thought Questionnaire (ATQ) for screening and baseline assessment.

RQ4: what types of psychological interventions (e.g., CBT, mindfulness, mood tracking) are incorporated in mHealth apps targeting perinatal and postpartum mental health?

We found three types of interventions in the reviewed studies: (I) health education modules; (II) training interventions; and (III) coping mechanisms.

Health education modules were used in 40% (6 out of 15) of the studies and included psychoeducational literature, e-learning modules, and health education. Brusniak et al. (18) provided accessible tutorials, while Lorenzen et al. (22) combined e-learning modules with psychoeducational texts and exercises. Progga et al. (25) explored psychoeducational materials such as articles, frequently asked questions (FAQs), and infographics to explain perinatal mental health concepts and self-care strategies. Kamarudin et al. (24) incorporated two modules delivering psychoeducational content through videos, infographics, notes, and worksheets to build awareness and knowledge.

Training interventions were reported in 46.7% (7 out of 15) of the articles and included audio-guided meditation, pregnancy-specific relaxation exercises, and positive psychology activities. Lorenzen et al. (22) provided breathing exercise audio files; Ward et al. (12) used the “Headspace” app for mindfulness practice, and the “SerenaMente Mamma” app (9) offered positive psychology exercises and guided meditation. Self-monitoring tools, such as mood-tracking logs and scheduled reminders, were incorporated to help users build awareness and reflective habits (25). “LoVE4MUM” (24) included a module with CBT-informed exercises and guided worksheets to reframe negative automatic thoughts.

Coping mechanisms were reported in 40% (6 out of 15) of the articles. Zuccolo et al. (6) incorporated gaming elements and Doherty et al. (17) provided animated well-being tips. Branjerdporn et al. (11) offered encouragement messages for distressed women, while Rizzi et al. provided mindfulness-based materials in various formats through a virtual coach (23). Progga et al. (25) found that perinatal and postpartum women engaged with peer-support forums and online communities, where they shared experiences, practical tips, and emotional support. “LoVE4MUM” (24) included a mood tracking feature that facilitated daily self-monitoring of emotional states, while a single-button option provided immediate access to support resources for acute distress.

RQ5: what specific features of mHealth apps have been developed to improve mental well-being outcomes during and after pregnancy?

In terms of app features, six types were observed: (I) mental health screening; (II) physical and mental well-being exercises and meditations; (III) health education; (IV) visual design elements; (V) healthcare support; and (VI) additional support features. Table 4 summarizes app features reported in the reviewed articles. As the reviewed studies did not employ component-level analyses, it is not possible to isolate which individual features drove observed mental well-being improvements. Instead, we describe the features present across the included apps to inform future hypothesis-driven research.

“SerenaMente Mamma” offers psychology-based exercises and mindfulness meditation (9). “Mater” includes relaxation exercises and support hotlines (11). “Bluebelly” facilitates EPDS administration (7). Sarhaddi et al. proposed early detection of loneliness by incorporating heart rate data collection (19). “Headspace” provides guided meditation (12). The “Motherly” app (6) incorporates psychoeducation to increase users’ understanding of their mental health and behaviors. More specifically, they incorporated gaming elements to help users track and plan activities related to health and mood management, and behavior monitoring to enhance user motivation and engagement. The app developed by Hantsoo et al. includes activity and mood tracking (20). “Positively Pregnant” offers self-assessments and informational texts (21). The app by Lorenzen et al. provides psychoeducational content and e-learning modules (22). “BrightSelf” (16,17) suggests mood-improving activities such as self-reporting of psychological well-being and supporting self-awareness and disclosure. “TreC Mamma” app, used by Rizzi et al. (23), focuses on physical and mental well-being exercises and meditation. Progga et al. (25) found that effective features across apps included anonymity, ease of access on mobile devices, peer-to-peer support, and personalized content feeds. “LoVE4MUM” (24) incorporated automated email reminders, evidence-based multimedia content, and bilingual delivery to enhance engagement, accessibility, and adherence.

Overall, the reviewed apps primarily delivered mindfulness-based interventions and self-guided modules, with fewer focusing on diagnostic capabilities or predictive modelling, suggesting that current mHealth development in this domain emphasizes symptom management over early detection.

RQ6: how did users rate the usability, satisfaction, and engagement of the reviewed mHealth apps?

User-reported usability, satisfaction, and engagement were generally positive across the included studies, though findings were limited in depth, sample size, and comparability across studies. Table 5 summarizes the evaluation methods used to capture these outcomes.

Several studies gathered user feedback through questionnaires and surveys. Lorenzen et al. (22) collected qualitative feedback from first-time mothers using a web-based intervention and found that while participants responded positively to the content, they expressed a clear preference for a smartphone-based format, citing ease of access as a key concern. This finding underscores the importance of platform choice in shaping user experience, independent of content quality. Barber et al. (21) assessed satisfaction with the “Positively Pregnant” app at two time points, 24 and 36 weeks of pregnancy, and reported varying levels of satisfaction across the two assessments, suggesting that user experience may fluctuate over the course of an intervention rather than remaining stable. Progga et al. (25) gathered perspectives from perinatal and postpartum women on a range of mHealth tools and found that features perceived most positively included anonymity, ease of access on mobile devices, peer-to-peer support, and personalized content feeds, pointing to the importance of both technical and social dimensions of user experience. Kamarudin et al. (24) planned to evaluate user experience through between-group changes in outcome measures rather than dedicated usability instruments, and results were pending at the time of publication.

Likert-scale satisfaction ratings were used in two studies. Ward et al. (12) gathered responses from five participants using the “Headspace” app, who generally rated the experience positively; however, the very small sample size precludes any meaningful generalization. Barber et al. (21) also employed Likert-type items alongside their broader survey approach, providing some quantitative grounding to the satisfaction data reported for “Positively Pregnant”.

Three studies employed the Mobile Application Rating Scale (MARS), a 28-item instrument assessing app quality across engagement, functionality, aesthetics, and information domains. Carissoli et al. (9) and Rizzi et al. (23) used MARS to evaluate their respective apps, while Zuccolo et al. (6) applied it alongside other measures. Rizzi et al. (23) additionally used the User Engagement Scale-Short Form (UES-SF), a 12-item self-report questionnaire rated on a 5-point Likert scale, providing a more granular assessment of engagement dimensions such as focused attention and perceived usability. The use of these standardized instruments in these studies represents a more rigorous approach to usability evaluation compared to the ad hoc survey methods employed elsewhere in the literature.

Perego et al. (7) used the Standardized Computer Usability Survey (SCUS) to evaluate the “Bluebelly” system and reported a total usability score of 84 out of 100 for EPDS management, which remained stable at approximately 80 across different screen sizes. This result suggests that the system offered a consistently accessible user experience regardless of device format.

Engagement and dropout data, where reported, present a more mixed picture. Zuccolo et al. (6) reported a high dropout rate of 47.8% (11 out of 23 participants) in the mHealth arm of their study, a figure substantially higher than the weighted meta-analytic average of 19.9% reported for individual psychotherapy (27). Notably, incorporating human feedback into the intervention reduced the dropout rate to 11%, highlighting the potential value of blended human-digital care models in sustaining user engagement. This pattern is consistent with broader findings in the digital mental health literature, where high attrition has been identified as a persistent challenge (28).

Overall, while the available user experience data suggest reasonable acceptability of the reviewed apps, the heterogeneity of evaluation approaches, the predominance of small and non-representative samples, and the infrequent use of validated, standardized instruments make it difficult to draw firm or comparable conclusions about user experience across the field. Future evaluations should routinely employ established instruments such as the MARS, UES-SF, and SCUS alongside clinical outcome measures to enable more rigorous and comparable assessments of mHealth usability and engagement.

RQ7: how effective are mHealth apps in improving maternal mental health compared to control groups and alternative interventions?

The mHealth apps part of the reviewed studies demonstrate potential as effective tools for supporting maternal mental health, particularly in the short term. However, many existing studies are feasibility or pilot trials, and comprehensive data on long-term effectiveness against control groups or established interventions remain limited.

Several studies reported positive outcomes regarding symptom reduction. Hantsoo et al. (20) conducted an RCT assessing a Mood Tracking and Alert (MTA) mobile app among pregnant women with depressive symptoms. An exploratory analysis found significant improvements in Patient Health Questionnaire-9 (PHQ-9) scores (F=7.87, P=0.001), GAD-7 scores (F=6.32, P=0.003), and self-reported daily mood scores (F=2.62, P=0.03) over 8 weeks. However, the authors cautioned that patients with depressive symptoms often improve over time, limiting causal claims regarding the app’s efficacy.

Barber et al. (21) found a significant reduction in subjective stress (η²=0.088, P=0.023) using the “Positively Pregnant” app, though no significant changes in anxiety or depression were observed. Ward et al. (12) observed decreasing EPDS score trends (from 9.0±4.3 to 5.2±3.1), but the small sample size (n=5) precluded statistical analysis. Perego et al. (7) reported decreased EPDS scores with the “Bluebelly” system, which combines app-based screening with psychologist interventions. A previous RCT cited by Carissoli et al. (9) found that mothers (n=80) using a mindfulness app for 30 days reported lower stress, anxiety, and depression compared to controls.

In the MTA app RCT (20), women in the intervention group rated their ability to manage their own health significantly higher than the patient portal control group (F=4.03, P=0.007). The MTA group also had significantly more mental health-related provider encounters (F=6.0, P=0.02), and women whose calls were triggered by app alerts were substantially more likely to receive mental health referrals (t=22.3, P=0.03). However, Barber et al. (21) could not attribute the observed stress reduction to the app itself due to the absence of a comparison group.

Several ongoing RCTs plan to evaluate apps against usual care or active controls. Branjerdporn et al. (11) described a protocol comparing the “Mater” mindfulness app against usual care, with EPDS scores at 6 months postpartum as the primary outcome. Kamarudin et al. (24) outlined a protocol comparing the “LoVE4MUM” app alongside standard care against treatment as usual alone. Zuccolo et al. (6) described a protocol comparing their app plus brief online CBT against an active control, with EPDS change from baseline to 8 weeks as the primary outcome. A major limitation identified in our review is the absence of completed formal efficacy trials comparing mHealth apps to established interventions such as face-to-face psychotherapy.

The effectiveness studies generally reported short-term findings (up to 8 weeks), and the literature currently lacks robust long-term evidence (20). Several ongoing protocols plan to assess longer-term effects.

In summary, while short-term data suggest that mHealth apps can significantly improve mental health symptoms (PHQ-9, GAD-7, and EPDS) and enhance clinical care delivery compared to basic control apps, definitive evidence on sustained symptom reduction and efficacy compared to traditional treatments requires the completion of current and future controlled, longitudinal trials.

RQ8: what are the current limitations and gaps in the literature regarding mHealth interventions for mental well-being in pregnant and postpartum women?

Our review identified several limitations and gaps in mHealth interventions for the mental well-being of pregnant and postpartum women. Some articles, such as Carissoli et al. (9), provide limited usability or performance data. Sarhaddi et al. (19) applied machine learning (ML) to a small dataset, limiting the model’s validity to healthy populations. The “Motherly” app (6) delivered CBT via phone, preventing not only visual assessments but also rapport building between the counselor and participants. Marcano Belisario et al. (16) relied solely on unvalidated mood-related questions.

Barber et al. (21) had a very small sub-sample for rigorous statistical analysis and lacked user-level daily usage data. Lorenzen et al. (22) reported a homogeneous sample and excluded partners from participation. Brusniak et al. (18) acknowledged demographic limitations due to language constraints, as the study was conducted in German, which likely excluded non-German-speaking participants from disadvantaged socio-economic groups. Such barriers, including linguistic ones, could be mitigated through the use of translated surveys (29).

Progga et al. (25) highlighted additional gaps, including the lack of formal efficacy trials, limited integration of validated screening tools into apps, small and demographically narrow samples, and the absence of long-term follow-up data. Similarly, Kamarudin et al. (24) reported limitations such as a small pilot sample size (n=72), reliance on self-report measures introducing risk of bias, potential low adherence, and limited generalization beyond a Malaysian urban tertiary-ward setting. Overall, these limitations provide useful guidance for future research on mHealth apps for maternal mental well-being.

Discussion

We conducted a systematic review to answer focused research questions on mHealth apps for maternal mental well-being. In particular, the review examined the types of apps developed and tested, the mental health issues they targeted, the screening tools employed in the apps, the psychological interventions delivered, the app features present across included studies, and comparative effectiveness versus control groups or alternative interventions.

We screened a total of 2,127 articles following the PRISMA method and selected 15 articles for inclusion. Eight RQs guided our analysis and were used to structure the findings on mHealth apps for the mental well-being of pregnant and postpartum women. The reviewed articles collectively addressed all RQs and provided insights into the current state of research on mHealth interventions for maternal mental health.

Interpretation of key findings

This systematic review identified and analyzed 13 mHealth apps designed to support maternal mental health during the perinatal and postpartum period. The most common assessment approach involved self-report questionnaires, particularly the EPDS, which appeared in 11 of the 15 included studies. This finding is consistent with the EPDS’s status as the most validated and widely adopted instrument for perinatal depression screening globally (26) and reflects its integration into standard clinical pathways in many countries. The 2022 WHO recommendations on maternal and newborn care explicitly endorse routine screening for perinatal mental health conditions, including depression and anxiety, using validated tools such as the EPDS (30), and a 2022 meta-analysis of 17 studies confirmed its excellent predictive validity across both pregnant and postpartum populations (31). In addition to screening, several apps incorporated evidence-based interventions such as CBT, mindfulness-based meditation and exercises, and mood tracking. CBT has demonstrated both short- and long-term efficacy for perinatal depression, anxiety, and stress across 79 RCTs (32), with a 2023 meta-analysis further confirming a medium overall effect size for CBT-based interventions on perinatal depressive symptoms (33). Mindfulness-based interventions have similarly shown superiority over control conditions for perinatal depression and anxiety across 25 RCTs (34), and increasing evidence supports their role in reducing stress and anxiety during pregnancy (3).

The predominance of mindfulness-based and psychoeducational content across the included apps aligns with broader trends in digital mental health, where low-intensity, self-guided interventions are favored for their scalability and accessibility (15). This is particularly relevant for perinatal populations, among whom barriers to accessing face-to-face care, including stigma, time constraints, and limited-service availability, are well documented (4,5). The finding that most apps were available on both Android and iOS platforms supports accessibility, although the near-absence of web-based options may limit reach for users without smartphone access, a consideration of relevance particularly in lower-resource settings (5).

The short-term effectiveness data identified in this review, including significant improvements in PHQ-9, GAD-7, and EPDS scores in the single completed RCT (20), are encouraging but should be interpreted cautiously. A prior systematic review by Sakamoto et al. (3) similarly found promising short-term psychosocial benefits of mHealth interventions for pregnant women and new mothers, while also noting the paucity of long-term evidence, a limitation that remains unresolved in the present synthesis. High dropout rates observed across mHealth studies in our review [up to 47.8% in one study (6)] are consistent with the wider digital health literature; Torous et al. (28) reported a pooled dropout rate of approximately 26% across smartphone app trials for depressive symptoms, compared to 19.9% for individual psychotherapy (27). These figures suggest that sustaining engagement with mHealth tools remains a fundamental challenge regardless of the clinical population.

The variety of screening methods identified 24 distinct tools across 15 studies, which underscores the absence of a standardized assessment framework in this field. While the EPDS is clearly the most consistently used instrument, the diversity of complementary tools applied alongside it limits cross-study comparability and makes it difficult to establish benchmarks for treatment response. These mirror concerns raised in broader mHealth research, where outcome measure heterogeneity has been identified as a key obstacle to evidence synthesis (15). A COMET-registered initiative is currently underway to develop a standardized core outcome set for perinatal anxiety trials (35), and Pettman et al. (33) have specifically called for a minimum core dataset for perinatal depression intervention studies, a challenge that is equally pressing for mHealth research in this area. The relative concentration on depression and anxiety, with stress addressed in only four studies and loneliness in only one (19), suggests that the full spectrum of perinatal mental well-being remains under-addressed by current apps.

User acceptability was generally positive across the included studies, with several reporting high satisfaction and perceived usefulness. This is consistent with findings from Bt Wan Mohamed Radzi et al. (8), who reported favorable attitudes toward mHealth tools among postpartum women, and supports the feasibility of digital approaches in this population. However, acceptability should not be conflated with efficacy, and the absence of completed formal efficacy trials comparing mHealth tools to established interventions such as face-to-face psychotherapy remains a critical gap.

Implications of findings

The findings of this review carry several practical implications for app developers, clinicians, and researchers working in perinatal mental health.

From a design perspective, the review identified several unmet needs in the current generation of mHealth tools. No single app simultaneously addressed depression, anxiety, stress, and loneliness, despite evidence that these conditions frequently co-occur in the perinatal period (1,4). Future apps should be designed to address multiple mental well-being dimensions within an integrated platform, rather than targeting isolated conditions. Similarly, only a minority of the included apps spanned both the prenatal and postnatal periods. Given that mental health risk is elevated across the full perinatal continuum (5), apps that support women from pregnancy through to the early postpartum period are likely to be more clinically valuable than those addressing only one phase.

The distribution of intervention types across health education modules, training interventions, and coping mechanisms was reasonably broad, but only one study integrated all three within a single app. Combining these complementary strategies within a single platform could provide more comprehensive and continuous support for expectant and new mothers. Meditation and relaxation exercises were similarly underrepresented as primary intervention components, despite their established role in managing perinatal anxiety and stress. A 2024 systematic review and meta-analysis by Abera et al. found that relaxation interventions during pregnancy significantly reduced maternal stress and anxiety (36), and a 2022 RCT demonstrated that an 8-week prenatal mindfulness programme significantly reduced stress, anxiety, and depression at 3 months postpartum (37). The incorporation of gamified design elements, as implemented by Zuccolo et al. (6), represents a promising avenue for enhancing user engagement and adherence that warrants further development and evaluation.

Considering the rapid development of artificial intelligence, automated detection approaches, such as the machine-learning-based loneliness prediction model proposed by Sarhaddi et al. (19), could complement traditional self-reported screening methods to improve data objectivity and reduce reliance on user-initiated input. Future apps should explore combining passive sensing with validated self-report instruments to capture a more complete picture of maternal mental health.

For clinicians, the current evidence base supports mHealth tools as low-intensity, accessible adjuncts to standard perinatal care rather than replacements for clinical assessment or psychotherapy. The short-term benefits observed for depression, anxiety, and stress outcomes suggest that these tools may be most useful for subthreshold or mild-to-moderate presentations, and for extending support between clinical contacts. Integration into care pathways, for instance, using app-based EPDS monitoring to trigger clinical review, as demonstrated by Hantsoo et al. (20), represents a practical model for blended care delivery.

For researchers, this review highlights the urgent need for adequately powered, controlled trials with active comparators, standardized outcome measures, and follow-up periods extending beyond 8 weeks. The adoption of core outcome sets for perinatal mental health research would substantially improve cross-study comparability (33). A COMET-registered initiative is currently underway to develop a standardized core outcome set for perinatal anxiety trials (35), and Pettman et al. (33) have specifically called for a minimum core data set for perinatal depression intervention studies, a recommendation that applies equally to mHealth research in this domain. Evaluations should routinely report adherence and engagement metrics alongside clinical outcomes, as these are critical to understanding the mechanisms by which mHealth tools exert their effects. Future work should also prioritize inclusivity, recruiting samples that reflect the sociodemographic and linguistic diversity of perinatal populations globally, including women from LMIC, where the burden of perinatal mental illness is disproportionately high (5).

In terms of evaluation methodology, our review identified six distinct approaches for assessing usability, satisfaction, and engagement, with simple questionnaires and the MARS being the most common. Employing well-established instruments such as the UES-SF and SCUS alongside clinical outcome measures would yield more rigorous and comparable usability data. Gathering user feedback iteratively and integrating it into subsequent app development cycles may further enhance long-term engagement and satisfaction. mHealth apps should also be evaluated under real-world conditions, with explicit attention to user privacy, data security, and the ethical handling of sensitive health information. These are considerations that are particularly important given the vulnerability of the perinatal population.

Limitations of the review

This review aimed to synthesize evidence on the design features, intervention approaches, and evaluation of mHealth apps for maternal mental well-being. Several limitations should be considered while interpreting its findings. This systematic review was not registered in any prospective register prior to conduct. No review protocol was prepared or made publicly available prior to conducting this review.

The search was limited to studies published in English and indexed in PubMed and Scopus. This may have excluded relevant work published in other languages or indexed in regional databases, limiting the geographical representativeness of the evidence base. The study focused on peer-reviewed literature and did not consider gray literature and commercial apps lacking published evaluations; consequently, the findings reflect primarily research-oriented interventions rather than the broader marketplace of maternal mental health tools.

There was substantial heterogeneity in study designs, theoretical models, and outcome measures across the included studies. This prevented formal cross-study comparisons and limited our ability to identify consistent evaluation frameworks. The inclusion of feasibility studies alongside evaluation studies reduced the availability of mature effectiveness data. Finally, no formal assessment of methodological quality was conducted; therefore, this review reports preliminary indications of potential efficacy rather than conclusive proof of effectiveness. Future reviews should incorporate a validated quality appraisal tool, such as the Mixed Methods Appraisal Tool (MMAT) or the Cochrane Risk of Bias tool, to provide a more rigorous evaluation of the evidence base.

Despite these limitations, the review offers an informed synthesis of how mHealth tools for maternal mental well-being are currently conceptualized, designed, and evaluated, and highlights clear directions for more standardized, inclusive, and theory-driven future development.

Conclusions

This systematic review synthesized evidence from 15 studies evaluating 13 mHealth apps designed to support maternal mental well-being during pregnancy and the postpartum period. Across the included studies, mHealth tools most commonly incorporated self-report screening (particularly the EPDS, which featured in 11 of the 15 studies) and delivered evidence-based interventions including CBT, mindfulness practices, psychoeducation, and mood or self-monitoring. These intervention types have established efficacy in perinatal populations from the broader clinical literature, and the apps in this review showed promise for short-term symptom management and were generally acceptable to users.

However, the evidence base remains constrained by small and homogeneous samples, short follow-up periods, highly heterogeneous outcome measures, and inconsistent reporting of engagement and adherence. Only one completed RCT was identified, and formal cost-effectiveness evaluations were absent entirely, limiting conclusions about sustained effectiveness, scalability, and integration into routine perinatal care pathways. Design gaps were also evident: few apps spanned both prenatal and postnatal periods, intervention diversity was limited, and no single app simultaneously addressed depression, anxiety, stress, and loneliness, conditions that frequently co-occur during the perinatal period.

Future research should prioritize adequately powered, longitudinal trials with diverse populations; adopt standardized, perinatal-specific outcome measures in line with emerging core outcome set initiatives; and develop multidimensional, user-centered app designs that integrate education, skill-training, and coping support across the full perinatal continuum. Evaluations should routinely capture adherence and engagement metrics alongside clinical outcomes and assess cost-effectiveness to inform policy and implementation. Progress along these lines will enable the development of evidence-based, scalable digital solutions capable of more effectively promoting the mental well-being of pregnant and postpartum women globally.

Acknowledgments

During the preparation of this manuscript, the authors used Anthropic’s Claude Sonnet-4.5 for language assistance and to improve the clarity and readability of the texts. After using this tool, the authors reviewed the text as needed and take full responsibility for the content of the publication.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-2025-72/rc

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-2025-72/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-2025-72/coif). B.M.C. serves as an unpaid editorial board member of mHealth from March 2025 to December 2026. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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