Eligibility criteria

This study followed the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines. A formal protocol was not prospectively registered. This scoping review adhered to established principles of systematic review and followed a rapid review methodology to efficiently screen and synthesize the literature. Peer-reviewed articles published in English between 2015 and May 2025 were included to capture developments in mobile and digital health technologies within the past decade. Studies were eligible if they focused on (1) Asian American populations, (2) maternal mental health, including PD or PPD, and (3) digital or mHealth interventions such as mHealth or telehealth.

In this review, maternal mental health is defined as the overall psychological and emotional well-being of women during the perinatal period [15]. Conversely, perinatal psychiatric illness refers to clinically diagnosable mental disorders, whether new or current, that occur during pregnancy or within the first year postpartum [16]. Specifically, PD encompasses depressive episodes occurring during pregnancy or within the first 4 to 8 weeks following childbirth [15], whereas PPD involves intense depressive symptoms that manifest after delivery and persist for more than two weeks [1].

Regarding study populations, Asian American participants were defined as individuals of Asian descent residing in the U.S. or those explicitly identified as such by study authors. In contrast, non-U.S. Asian participants were identified based on their specific national or regional backgrounds as reported in the literature. To ensure a comprehensive evidence base, studies were categorized into two overlapping groups: those focusing on Asian Americans and those involving non-U.S. Asian or mixed populations using mHealth interventions for PD or PPD. Studies meeting both criteria were included in both categories to capture both population-specific nuances and broader intervention trends.

Eligible studies included qualitative, quantitative, and mixed-method research with full-text availability. Studies that addressed PD were included to provide a broader understanding of maternal mental health challenges. Articles that focused on general telehealth services were included only if they specifically examined mHealth apps. Conference abstracts, dissertations, theses, and non-peer-reviewed literature were excluded during the initial screening. These criteria were developed a priori to demonstrate methodological rigor.

Search strategy

A scoping review was conducted in May 2025 using PubMed, EBSCOhost, Google Scholar, and manual reference searches following the principles of systematic review and rapid review methodology. The search strategy included combinations of keywords and Medical Subject Headings (MeSH) terms such as “Asian American,” “postpartum depression,” “perinatal depression,” “mHealth,” “mobile health,” and “telehealth”. Boolean operators and truncation symbols (“Asia”[MeSH] OR “Asian”[MeSH] OR “Asian People”[MeSH] OR “Asian American”[tiab] OR “Asian American”[MeSH]) AND (“Depression, Postpartum”[MeSH]) AND (“Telemedicine”[Mesh] OR “Mobile Applications”[MeSH] OR “Smartphone”[MeSH]) were used to refine and expand the search scope.

Out of 246 records identified through database and manual searches, 67 duplicates were removed. Screening the remaining 179 titles and abstracts led to the exclusion of 103 records. A total of 76 articles underwent full-text review, resulting in 25 studies selected for final inclusion. The study selection process is illustrated in Figure 1. Among these, seven focused specifically on Asian American women experiencing depressive symptoms during the perinatal or postpartum periods, and 18 examined mHealth interventions for maternal depression more broadly. The remaining 51 articles were excluded for reasons including a lack of focus targeted at Asian American populations or a primary emphasis on non-mobile platforms, such as general telehealth or in-person therapy.

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Figure 1. PRISMA flow diagram for the scoping review. Adapted from “PRISMA” (https://www.prisma-statement.org/). This work is licensed under CC BY 4.0.

Data extraction and synthesis

A standardized data charting form was developed in Microsoft Excel and iteratively refined by the research team to ensure comprehensive and consistent data extraction. In alignment with PRISMA-ScR guidelines, the following variables were charted for each included source: bibliographic information (author, year, country); study characteristics (design, setting); population details (Asian subgroup, sample size, perinatal stage); intervention characteristics (mHealth modality, duration, cultural/linguistic adaptation); and primary outcomes (clinical effectiveness, feasibility, and engagement). Discrepancies were resolved by discussion and consensus.

A total of 246 records were identified through database and manual searches. After removing 67 duplicates, 179 records remained for title and abstract screening. Of these, 103 articles were excluded for reasons including irrelevant study outcome/design (n = 72), non-English publication (n = 1), being published before 2015 (n = 29), or being a dissertation (n = 1). Seventy-six full-text articles were assessed for eligibility. Of these, 51 articles were excluded because they did not target Asian American populations or focused solely on general telehealth, web-based platforms, or in-person therapeutic interventions. The final synthesis included 25 studies: seven focused specifically on Asian American women with depressive symptoms during the perinatal or postpartum period, and 18 examined the use of mHealth interventions for PD/PPD.

Extracted data were synthesized using both descriptive statistics and narrative thematic analysis. Descriptive synthesis included counts of studies by year of publication, study design, perinatal stage, mHealth modality, Asian subgroup representation, and outcome measures. For example, mobile apps and short message service (SMS)/text messaging were the most frequently described mHealth modalities, and Edinburgh Postnatal Depression Scale (EPDS) and Patient Health Questionnaire (PHQ) were the most common outcome measures. Thematic synthesis summarized key patterns in intervention components (e.g., psychoeducation, symptom monitoring, cognitive behavioral therapy (CBT)-informed strategies, peer or coach support), feasibility/acceptability findings, and cultural/linguistic adaptations. Consistent with scoping review methodology, quality appraisal of included studies was not performed, as the primary aim was to map the extent and characteristics of the literature rather than to evaluate study quality or perform meta-analysis. A formal protocol was not registered for this scoping review; however, the study followed the PRISMA-ScR guidelines. Future studies may consider prospective registration to enhance transparency and reproducibility.

Thematic findings

Thematic analysis was initiated with the assistance of ChatGPT-5 (OpenAI, [Aug 14 version]) to systematically identify recurring patterns and concepts within the dataset. The purpose of this initial step was to leverage the AI’s ability to process large amounts of textual data efficiently and highlight potential themes for deeper human investigation. The prompt sequence included the following steps: 1) summarize the key findings included in the literature review table and create codes; 2) group similar codes into broader categories; and 3) group categories into major themes. The thematic analysis of the remaining 25 studies revealed six major themes that were then meticulously reviewed, validated, and refined by the researchers: Access; Modality; Effectiveness; Engagement; Social Connectivity; and Implementation. These themes illustrate the factors influencing uptake, effectiveness, and user experiences of mobile, web-based, and telehealth interventions across diverse populations. No AI tools were credited as authors, in accordance with journal AI authorship policies.

Study characteristics

This review included 25 sources of evidence. The characteristics of the included studies are summarized in Table 1. The study designs comprised observational studies (n = 6, 24%), systematic reviews or meta-analyses or review study (n = 6, 24%), qualitative studies (n = 4, 16%), pilot studies (n = 3, 12%), randomized controlled trials (n = 3, 12%), study protocols (n = 2, 8%), mixed-method study (n = 1, 4%). The EPDS was the most frequently reported validated outcome measure, appearing in 17 of 25 studies (68%). Other measures included the PHQ in 7 studies (28%) and the Generalized Anxiety Disorder scale (GAD-7) in 2 studies (8%). Other measures reported across the included studies included the Beck Depression Inventory, Beck Anxiety Inventory, Kessler Psychological Distress Scale/Kessler-10, Center for Epidemiologic Studies Depression Scale, Somatic Symptom Scale-8, Insomnia Severity Index, and other study-specific measures.

Synthesis of results

Access. Studies identified structural and cultural barriers to care. Language barriers, cultural stigma, and provider bias restricted engagement with interventions among immigrant and minority populations [14, 20–22]. For example, high-risk immigrant women often could not access app-based care coordination or referrals due to limited language options [21], while some preferred app-based services rather than visiting health care providers due to language barriers. mHealth preferences allowed more flexibility to understand their language and to express their mood changes [20]. Similarly, stigma surrounding mental illness as a sign of weakness, the cultural value placed on ‘saving face,’ and superstitions linking it to sinful actions against ancestors or God contribute to reluctance towards seeking professional mental health support and may limit the acceptance of e-mental health interventions [14]. Furthermore, somatic symptom presentation and insufficient culturally sensitive tools limit effective engagement with healthcare providers. These barriers were further exacerbated during the COVID-19 situation when increased anti-Asian racism contributed to reduced access to care [22].

Modality. Digital interventions, specifically mindfulness-based and CBT-based apps, demonstrated efficacy in reducing depressive and anxiety symptoms [17, 18, 26, 28]. Hybrid approaches combining asynchronous digital content with synchronous or in-person support enhanced flexibility and allowed interventions to reach broader, more diverse populations [18, 34]. These hybrid delivery models addressed barriers such as geographic, socioeconomic, and systemic limitations. Systematic review reported blended methods of CBT and telephone-based support as beneficial for treating PPD, while internet-based CBT (iCBT) did not demonstrate significant efficacy [26]. Meanwhile, a study examined a fully automated, smartphone-delivered CBT program aimed at reducing depression before and after childbirth [24]. A study reported large-scale intervention implementation without the need for direct therapist involvement can help prevent major depressive disorders, including early identification of ‘baby blues’ symptoms. Notably, some users expressed a preference for face-to-face interactions alongside digital modalities, indicating the importance of flexible delivery options [20].

Effectiveness. Overall, digital interventions demonstrated efficacy in reducing depressive and anxiety symptoms, and promoting mental health awareness [17, 18, 26, 28]. Mindfulness-based apps and CBT interventions were particularly effective in mitigating perinatal and postpartum depressive symptoms. An iCBT program reported decreased depressive symptoms (F = 61.69, P < 0.001), increased behavioral activation (F = 55.05, P < 0.001), and lower dysfunctional automatic thoughts (F = 29.21, P < 0.001) [34]. A randomized controlled trial, mobile based app study, exhibited significant positive results from pre- to post-intervention, with a modest effect and 4% variability in score, and improved depressive symptoms regardless of the intervention and control group [26]. EPDS scores (F = 23.53, P < 0.001) declined over time in virtual and in-service visiting participants, contributing to decreased depression rates. However, in-person treatment led to improved maternal-infant bonding than the virtual sessions (F = 5.93, P < 0.01), resulting in a moderate treatment effect [17]. Interventions also supported psychosocial outcomes, including enhanced parenting competence and maternal self-efficacy among primiparous women (F = 5.93, P < 0.05) compared to multiparous women (F = 0.413, P = non-significant) and substantial improved social support accounting with approximately 41% of the variation in scores, reflecting a strong effect, however, improvement was mixed in some studies [35, 40].

Engagement. User engagement varied across interventions, influenced by factors such as mental health status, motivation, time constraints, and trust in online platforms [20, 27, 37]. For instance, Chinese immigrant women preferred social media apps over text messaging as the former provided flexibility and freedom to extract information when required [20]. In rural India, women used local, user-friendly language to record information via an interactive voice response (IVR) system for managing PD and adapting coping strategies [25]. Participants generally found the interventions to be feasible, acceptable, and usable, with a preference for culturally tailored content and mobile access [31, 32, 39].

Social connectivity. Digital interventions provided both peer and professional support, which reduced isolation and facilitated knowledge sharing [19, 31, 39]. Personalized content, including daily prompts, tailored messaging, and referral support, improved engagement and encouraged health-seeking behaviors [20, 28, 30]. Mobile apps have helped women with holistic resources such as information on daily check-ins, mental health tracking, and self-care information [31]. Chinese immigrant women viewed social media apps as a valuable tool for communicating with healthcare providers and raising awareness about PD [19]. Meanwhile, the integration of mHealth apps in routine healthcare has increased referral success rates of identified PPD, from 5.89% to 57.39% representing a nearly tenfold improvement in eight years (2014–2022), enhancing health-seeking behaviors among postpartum women in the Asian community [30]. Online support groups and social media features allowed participants to connect with others who shared similar experiences, enhancing motivation and empowerment, while a few lacked online participation due to underlying PPD symptoms, like reluctance to share identity or self-criticism [37].

Implementation. Integration of digital interventions into healthcare systems increased reach, improved referral uptake, and promoted population-level impact [29, 30, 34]. Scalability and cost-effectiveness were important considerations, with several studies noting that brief, structured interventions delivered digitally could extend support to large and diverse populations while overcoming traditional resource constraints [28, 29]. mHealth app-based CBT for PPD helps bridge the gap in mental health resources by addressing the shortage and disparities faced by women, stigmatization around PPD, and challenges of postpartum recovery [28]. Likewise, integration of a brief, scalable intervention into standard prenatal care can help to reduce the incidence of PD effectively [29].

Strengths and limitations

This review identifies important gaps in the development of mHealth tools, which are often designed for general perinatal populations addressing depressive symptoms rather than being culturally and linguistically tailored to the unique needs of Asian American women. A key strength is its specific focus on Asian American women, a population that remains underrepresented in maternal mental health and mHealth research. While much of the existing literature broadly addresses perinatal and PPD, few studies specifically examine the fastest-growing racial group in the U.S. By bringing attention to these disparities, this study strengthens the equity-focused dialogue in maternal mental health research.

The review is limited by the small number of U.S. based studies focused specifically on Asian Americans, often relying on international Asian data that may not reflect the unique immigrant experience in the U.S. Although cultural similarities exist across Asian populations, Asian American women face distinct barriers related to immigration, language access, racial discrimination, and healthcare delivery within the U.S. system. Consequently, concluding international Asian contexts may oversimplify heterogeneity within Asian American communities underscores a critical gap in the current evidence base.

Conclusion

To promote equitable health outcomes, mHealth tools must be embedded within existing healthcare structures and explicitly designed for the lived experiences of Asian American women. While significant literature exists regarding general health interventions for the burgeoning U.S. Asian immigrant population, there is a notable gap in studies addressing mHealth apps for perinatal and PPD in this demographic. This presents an opportunity to address critical questions about how technology can be embedded and leveraged to support maternal mental health care and promote equitable health outcomes among diverse women in the U.S.