Introduction
The perinatal period, spanning from pregnancy through the first postpartum year, constitutes a time of heightened vulnerability for the family system—particularly for mothers (Leach et al., 2017). This stage involves substantial psychological and physical changes (Al-abri et al., 2023), which may increase the risk of perinatal mental health problems, including depression. Globally, the prevalence of perinatal depression has been estimated at approximately 17% (Shorey et al., 2018), increasing to around 29% during the COVID-19 pandemic (Caffieri et al., 2024). The societal costs associated with perinatal depression are therefore considerable (£75,728 per mother–child dyad; Bauer et al., 2016). Despite this burden, perinatal depression remains underdiagnosed, undertreated, and often treated below evidence-based standards (Motrico et al., 2022).
The prevention of perinatal depression may help to reduce the adverse consequences associated with this condition (O’Connor et al., 2019). Primary preventive interventions for perinatal depression encompass strategies aimed at promoting mental health, reducing modifiable risk factors, and strengthening protective factors in women without a clinical diagnosis (Arango et al., 2018; Conejo-Ceron et al., 2017; Moreno-Peral et al., 2017). The primary prevention of maternal perinatal depression is considered a priority by the World Health Organization (WHO; Rahman et al., 2018; World Health Organization, 2015) and by other international health organizations (Curry et al., 2019). Several non-pharmacological preventive interventions have shown effectiveness in reducing the risk of perinatal depression in women, including psychological interventions (Martín-Gómez et al., 2022), nutritional supplementation (Miller et al., 2013), physical activity programmes (Carter et al., 2019), and other approaches such as massage and music therapy (Zhu et al., 2021).
Over the past decade, we have been witnessing a revolution in the number of articles published annually around the world. It has been estimated that around 2.5 million papers are published each year (Conroy, 2019). In this context, the proliferation of systematic review (SRs) and meta-analyses (MAs) has also increased considerably. Between 1986 and 2015, a total of 266.782 SR and 58.611 MAs studies were indexed in PubMed (Ioannidis, 2016). This corresponds to a marked annual increase in publication of 2.73% SRs and 2.64% MAs studies between 1991 and 2014 (Ioannidis, 2016).
A similar increase has been observed in SRs and MAs examining the effectiveness of preventive interventions for perinatal mental health. By the end of 2022, an umbrella review had identified 19 SRs and MAs encompassing 152 randomized controlled trials (RCTs) testing preventive interventions for perinatal depression (Motrico et al., 2023). To manage this amount of data, novel methods to aggregate and update information are needed. One such methods is the so-called ‘Meta-Analytic Research Domain’ (MARD), a living SR and MA aiming to aggregate evidence of a whole field (Cuijpers et al., 2022). Rather than addressing a single, narrowly defined meta-analytic research question, MARD encompasses an entire research area by integrating multiple research questions within the same research area or domain. As in living systematic reviews, searches are updated periodically so that newly emerging evidence can be incorporated and meta-analysed (Cuijpers et al., 2022).
One pioneering example of MARD is the ‘Metapsy: Meta-Analytic Psychotherapy Databases’ (The Metapsy Collaboration, 2026) which develops open-access meta-analytic databases that synthesise evidence across a range of mental health conditions, such as depression, anxiety, and psychosis, and updates them periodically. The Metapsy also provides a set of meta-analytic tools that facilitate the calculation of pooled effect estimates and other meta-analytic statistics using the online databases available on the project website (see, for examples, Cuijpers et al., 2025; Harrer et al., 2025).
Incorporating patient and public involvement (PPI&E) into MARD design may also be a key element for maximizing its relevance and usability in the prevention of perinatal depression (INVOLVE, 2012). PPI, defined as research carried out in collaboration with or by members of the public, including patients, carers, health and social care service users, and representatives of health and social care organizations (Hayes et al., 2012), is essential for developing truly impactful, evidence-based interventions, as it can capture public needs and take them into account throughout the research process. Integrating these perspectives may benefits both stakeholders and researchers and may enhance the relevance of research outputs (Brett et al., 2014; Canfield et al., 2022; Weirauch et al., 2026). Despite its potential, the inclusion of PPI in SRs and MAs research remains limited (Zhou et al., 2024).
The aim of this study is to present the methodology of a MARD designed to synthesise the evidence on effectiveness of preventive non-pharmacological (psychological, educational, psychosocial and behavior change) interventions for perinatal depression, and to describe the process of co-refinement of an open-access, user-friendly open-access perinatal depression meta-analytic web-based tool. This MARD is intended to be implemented as an open-access, user-friendly web-based tool for researchers, clinicians, and members of the public. In this way, incorporating PPI into the refinement of the MARD sustains the collaborative knowledge production and enhance the relevance, usability, and potential real-world impact.
Methods
This study will comprise two phases. In phase 1, a MARD will be designed and developed. We will systematically and periodically search for and collect data from RCTs on the prevention of perinatal depression. This protocol followed PRISMA-P (Moher et al., 2015; see suplementary material and Gómez-Gómez et al. 2026) and was previously registered in PROSPERO (ID: CRD42023432002) on 31 July 2023. This study is conducted within the Metapsy project (https://www.metapsy.org/; Harrer et al., 2025). In addition, in phase 2, also conducted within the context of the Metapsy project, the open-access Metapsy web-based meta-analytic tool for perinatal depression will be co-refined with stakeholders (health professionals and lived experience users – e.g., woman in the perinatal period-) through a PPI approach.
Phase 1: MARD design and development
Eligibility criteria
Table 1 shows inclusion and exclusion criteria. Regarding inclusion criteria, studies targeting women in the perinatal period (from pregnancy to one year postpartum) will be included. Both adult (≥ 18 years old) and adolescent populations (< 18 years old) will be eligible, with no restrictions on gestational age or the specific perinatal phase at enrolment. Studies focused on perinatal women with a current diagnosis of a major depressive disorder or with elevated symptoms, as determined by a diagnostic interview or a cut-off score on a questionnaire will be excluded, as well as studies focused on participants currently receiving treatment for a depressive episode at baseline.
Table 1. Inclusion and exclusion criteria.
| Inclusion criteria | Exclusion criteria |
|---|
| Population
| Women during the perinatal period (from pregnancy to one year postpartum) | Women with a current major depressive episode, as determined by a diagnostic interview or a cut-off score on a questionnaire, or currently receiving treatment for depression at baseline |
| Intervention
| Preventive non-pharmacological (e.g. psychological, educational, psychosocial and behavior change) interventions which aim to prevent perinatal depression (incidence and/or reduction of symptoms) | Any other type of intervention |
| Comparator
| No intervention, usual care, waiting list and attention control | Any type of intervention with available evidence of its effectiveness in preventing perinatal depression |
| Outcome
| Prevention of perinatal depression (incidence and/or reduction of symptoms), assessed using validated self-report instruments or standardized diagnostic interviews | Different outcomes or trials in which the effects on perinatal depression and other diseases are aggregated |
| Study design
| Randomised controlled trials or pilot trials or cluster trials or randomized clinical trials, or clinical trials | Other designs |
| Language
| All | None |
| Setting
| All | None |
Eligible interventions will be preventive non-pharmacological interventions targeting perinatal depression (universal, selective and indicated). This will include psychological (e.g., cognitive behavioural therapy), educational (e.g., self-help manuals), psychosocial (e.g., social support) and behavior change (e.g., exercise-based; diet or sleep) interventions. Multi-arm trials will also be considered.
Only RCTs will be included, as they represent the reference standard for evaluating intervention efficacy and provide stronger evidence of causality than other study designs. Eligible RCTs will be required to evaluate the prevention of perinatal depression operationalized as either a reduction in the incidence of new depressive episodes and/or a reduction in perinatal depressive symptoms severity. All reported outcomes related to depression will be extracted, regardless of whether they are designated as primary or secondary outcomes in the original studies. Outcomes must be assessed using validated self-report measures (e.g., the Edinburgh Postnatal Depression Scale (EPDS); Cox et al., 1987), or standardized diagnostic interviews (e.g., Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I).
Regarding control groups, waiting list, care as usual, no intervention, or attention control conditions (defined as an intervention with no demonstrated preventive effect or placebo) will be considered. No restrictions will be applied regarding language, setting, or year of publication.
Information sources
The literature search will be conducted across multiple electronic databases, including PubMed, Scopus, PsycINFO, CINAHL (via EBSCO), OpenGrey (via DANS), and Embase. In addition, hand searching will be performed by examining the reference lists of all included studies, as well as those of relevant previous reviews, to identify any further eligible studies.
Search strategy
The search strategy will be developed using keywords and MeSH terms related to the perinatal period, perinatal depression, prevention, and RCTs, which will be combined using Boolean operators. Searches will cover the period from database inception to the present, with annual updates conducted to incorporate newly published evidence.
Table 2 presents the search strategy developed to be used in PubMed. The full search strings, adapted for each database, are provided as supplementary material.
Table 2. PubMed search strategy.
| ((((((((((((postpartum [Title/Abstract]) OR postnatal [Title/Abstract]) OR puerperal [Title/Abstract]) OR perinatal [Title/Abstract]) OR prenatal [Title/Abstract]) OR antenatal [Title/Abstract]) OR intrapartum [Title/Abstract]) OR pregnancy [Title/Abstract]) OR “pregnancy”[MeSH Terms]) OR “pregnant women”[Title/Abstract]) OR “pregnant women”[MeSH Terms]) OR matern*[Title/Abstract] OR Patern*[Title/Abstract] OR “postpartum”[tw] OR “post-partum”[tw] OR “post partum”[tw] OR “puerperium”[tw] OR “Perinatal care”[Mesh] OR “perinat*”[tw] OR “postnat*”[tw] OR “maternal”[tw] OR “paternal”[tw] OR “Prenatal Care”[Mesh] OR “prenatal”[tw] OR “Prenatal education”[Mesh] OR “Puerperal Disorders”[Mesh]) AND (“depressive disorder”[MeSH Terms] OR “depressive disorder” [All Fields] OR depress* OR “depression”[MeSH Terms] OR “anxiety”[MeSH Terms] OR “anxiety”[All Fields] OR “mental health”[MeSH Terms] OR “mental health”[All Fields] OR “mental disorders”[MeSH Terms] OR “mental disorder”[All Fields]) AND ((prevent*[Title/Abstract]) OR (incidence [Title/Abstract]) OR (reduction*[Title/Abstract]) OR (symptom*[Title/Abstract]) OR (intervention*[Title/Abstract])) AND (“randomized controlled trial”[Publication Type] OR “trial”[Title/Abstract]) AND (randomizedcontrolledtrial [Filter]) |
Study selection
All records identified through the searches will be imported into Rayyan Advanced to support deduplication and study selection. Two reviewers will independently screen titles and abstracts, followed by full-text assessment of potentially eligible studies. Any disagreements will be resolved through discussion or consultation with a third reviewer. Reasons for exclusion at the full-text stage will be documented.
Study records
Data from the included studies will be extracted independently by pairs of reviewers, with disagreements resolved through discussion and, if necessary, by a third reviewer. Extracted data from each study will be recorded in data extraction sheet adapted from Cuijpers et al. (2022) to suit the objectives of the present study. To ensure accuracy of data extraction, a pilot extraction will be conducted on 10 articles prior to the full data extraction to refine the extraction form.
Data items
For the qualitative synthesis, the data to be extracted will include study characteristics (study ID, publication year, and country); details on the study arms, including descriptions of the intervention and control conditions and any additional arms in multi-arm trials; and information on outcomes, including outcome type (e.g., dichotomous or continuous - mean and standard deviation-), the instrument used to measure perinatal depression, the type of perinatal depression prevention outcome (e.g., symptom reduction or incidence), measurement time points, and rating type. Information on the interventions will also be collected, including type of prevention (e.g. universal, selective or indicated), exclusion of participant with depression at baseline (yes; no), format (e.g. individual, group, other), number of sessions, providers (e.g. psychologists, midwives), recruitment setting (e.g. clinical, community), and timing (e.g. prepartum, postpartum, prepartum and postpartum). Participant characteristics, such as target population (e.g., pregnant women, women during postpartum, or pregnant and postpartum women), mean age, previous deliveries, sample size, and prior history of depression, will be recorded.
For the quantitative synthesis, we will primarily extract the mean, standard deviation, and sample size for each follow-up assessment when outcomes are continuous. When outcomes are categorical, we will primarily extract the number of participants meeting the criteria for perinatal depression and the total sample size at each follow-up. If these statistics are not reported, other available statistical information that allows the calculation of standardized effect sizes will be extracted (e.g., change scores, p values, t-values; Harrer et al., 2021).
Outcome measures and prioritization
The primary outcomes will be the incidence of perinatal depression or the severity of perinatal depressive symptoms at the various follow-up points reported in the primary studies (e.g., post-intervention and subsequent follow-ups), as measured by structured clinical interviews or standardized self-report scales. Mid-intervention follow-up assessments will not be considered.
When more than one instrument is used to assess perinatal depression within the same study, a predefined hierarchical approach will be applied. Specifically, priority will be given to the instrument most frequently used across the included studies within the target population. If none of the instruments used in a given study are commonly employed across studies, the measure with the strongest evidence of validation for the country and clinical setting in which the study was conducted will be selected. This approach allows the inclusion of all eligible studies regardless of the specific assessment instrument used, thereby maximizing statistical power and representativeness of the meta-analytic estimates (Gates et al., 2022).
When studies report combined outcomes for perinatal depression and other conditions (e.g., anxiety), corresponding authors will be contacted to request disaggregated data. If separate data for perinatal depression cannot be obtained or authors do not respond, the study will be excluded from the MARD. No outcomes other than perinatal depression will be considered.
Risk of bias assessment
The risk of bias (RoB) in the included studies will be assessed using the Cochrane Collaboration’s Risk of Bias tool, Version 2 (RoB 2; Sterne et al., 2019). RoB assessment will be conducted independently by pairs of reviewers who will receive training in the application of RoB 2 provided by an experienced researcher (CM). Disagreements will be resolved through discussion, and if consensus cannot be reached, a third reviewer will adjudicate. The following five domains will be evaluated: (1) bias arising from the randomization process, (2) bias due to deviations from intended interventions, (3) bias due to missing outcome data, (4) bias in measurement of the outcome, and (5) bias in selection of the reported result. A RoB category will be assigned to each domain (low risk, some concerns, high risk), together with an overall risk category for each included study.
Data synthesis
Standardized mean differences (Hedges’ g) and their standard errors will be calculated for each primary study. Different statistical models will be offered by the meta-analytic web-based tool to pool effect sizes: DerSimonian-Laird, Restricted Maximum Likelihood, Maximum Likelihood, Hunter-Schmidt, Sidik-Jonkman, and Paule-Mandel estimators. Analyses will be able to be optionally adjusted using the Knapp-Hartung procedure. In addition, numbers-needed-to-treat (NNT) will be calculated using the method proposed by Furukawa & Leucht, (2011). Heterogeneity will be quantified using the I2 statistic and its 95% confidence interval. I2 will be interpreted as follows: 0% indicates no observed heterogeneity, with 25%, 50%, and 75% representing low, moderate, and high heterogeneity, respectively (Higgins et al., 2019). Prediction intervals will also be calculated to estimate the range in which the true effects of 95% of similar future studies are expected to lie.
Sensitivity and moderator analyses may be conducted using the options provided by the meta-analytic web-based tool, based on previously extracted study characteristics, including year of publication, country, sample size, age category, target population, number of previous deliveries, recruitment setting, intervention type, intervention format, number of sessions, perinatal timing of the intervention (e.g., pregnancy, postpartum, pregnancy & postpartum), intervention providers, prevention type, prior history of depression, type of control group, and RoB.
Small-study effects/publication bias
Publication bias will be assessed through visual inspection of funnel plots and by applying Duval and Tweedie’s trim-and-fill procedure (Duval & Tweedie, 2000). Egger’s test (Stuck et al., 1998) will additionally be performed to detect asymmetry in the funnel plot and calculate the corresponding p-value. Additionally, p-curve will be calculated using only significant effect sizes to determine whether there is a true effect underlying the meta-analysis data (Harrer et al., 2021).
Future studies derived from the MARD
This protocol constitutes the overarching protocol for the perinatal depression MARD within the Metapsy project. In addition to the primary pairwise meta-analytic synthesis, the resulting database may support future secondary analyses, including network meta-analysis and component network meta-analysis. Protocols for these future studies will be prospectively registered in an open science repository, where appropriate.
Phase 2: Co-refinement of the open-access Metapsy web-based meta-analytic tool for perinatal depression
The main aim of this second phase is to iteratively refine, in collaboration with stakeholders, the Metapsy tool for preventing perinatal depression—including its different sections (e.g., the Treatment Effect Explorer). To achieve this, a stakeholder-informed refinement process will be carried out, through a PPI approach. Stakeholders will be experts from different disciplines (e.g. psychology, medicine, and nursery) and women with lived experience relevant to the perinatal period. Participants will be recruited using purposive sampling to ensure variation in professional background (e.g. researchers, clinicians) and lived experience relevant to the perinatal period. Prior to the initiation of phase 2, approval will be obtained from the Information System of the Research Ethics Committees of Andalusia, Spain (Sistema de Información de los Comités de Ética de la Investigación de Andalucía, “SICEA”). All participants will be required to provide written informed consent before enrolment in the study.
An iterative refinement process will be conducted through three online focus groups. Each focus group will include around 7 to 10 participants (Leung & Savithiri, 2009). Separate focus groups will be conducted for expert stakeholders and women with lived experience. In the first focus group, participants will be presented with the initial prototype of the web-based meta-analytic tool. Feedback from the first round will be used to inform usability, content, and design improvements, after which a second focus group will be conducted to gather responses to the revised version. A third focus group will then be conducted to review and discuss the subsequent iteration of the tool and to identify any final refinements required before implementation. After each focus group, suggested modifications will be reviewed by the research team and incorporated into the next iteration of the tool where feasible and consistent with the aims of the study.
The focus groups will be conducted online via the Microsoft Teams platform (Microsoft Corporation, nd; Version 1415/24061318408) and will be recorded to support facilitation and note-taking. Data from the focus groups will be analysed using an open deductive framework analysis (Gale et al., 2013) to identify stakeholder feedback relevant to usability, accessibility, comprehensibility, and perceived usefulness. To enhance rigour, a subset of transcripts will be independently coded by two researchers, with discrepancies discussed and resolved through consensus. The Guidance for Reporting Involvement of Patients and the Public – Short Form checklist (GRIPP-2; Staniszewska et al., 2017) will be used to report PPI, and the Consolidated criteria for reporting qualitative research (COREQ; Tong et al., 2007) checklist will be followed to ensure transparent reporting of qualitative study methods and findings.
The final refined tool will be made openly accessible on metapsy.org (https://www.metapsy.org/). This will allow researchers, clinicians and women with lived experience to access and interact with up-to-date meta-analytic evidence on preventive non-pharmacological interventions for perinatal depression.
Discussion
The Metapsye-Perinatal study is being conducted within the ‘Metapsy: Meta-Analytic Psychotherapy Databases’ (The Metapsy Collaboration, 2026). Phase 1 describes the methodology for developing a MARD to synthesise evidence on the effectiveness of preventive non-pharmacological interventions for perinatal depression. Phase 2 describes the stakeholder-informed refinement process of an open-access, web-based meta-analytic tool for perinatal depression, intended to improve access to and use of the MARD outputs. Metapsy is designed as an open, updateable evidence infrastructure that supports the synthesis of mental health trials across broad research domains.
Perinatal depression is a major public health problem causing significant impact on women and their family system (Leach et al., 2017). Despite this, perinatal depression is still an under-recognized problem in many countries (Fonseca et al., 2020). Prevention should therefore be considered an important strategy to reduce its incidence and associated burden (Motrico et al., 2023).
In recent years, numerous primary studies, SRs and MAs have examined the effectiveness of preventive interventions for perinatal depression (Huang et al., 2020; Lewkowitz et al., 2024; Martín-Gómez et al., 2022; Suradom et al., 2021; Thompson et al., 2024). However, integrating this growing and heterogeneous body of evidence within a single, coherent, and updateable structure remains challenging. The large volume of existing evidence syntheses and the inconclusive or heterogeneous nature of the available findings, supporting the need for higher-level and more integrated approaches to evidence synthesis in this field (Motrico et al., 2023).
Within this context, MARD represents an innovative approach for synthesising evidence across a broad and evolving research field (Cuijpers et al., 2022). MARDs offer several advantages over other approaches used to synthesize evidence, such as living SRs, umbrella reviews, pair-wise meta-analyses, and network meta-analyses. Unlike these methods, MARDs follow a dynamic process that is continuously updated. Additionally, while umbrella reviews, pair-wise meta-analyses, and network meta-analyses typically address only a single research question, MARDs can encompass multiple research questions, allowing them to cover an entire field of study (Cuijpers et al., 2022). Compared with umbrella reviews, MARDs provide a standardized methodology in terms of inclusion criteria, screening procedures, data extraction, and RoB assessment. In contrast to living SRs, MARDs synthesize quantitative information and permit the execution of all relevant MAs within a given field.
Despite these advantages, MARDs also present some disadvantages, as they require substantial economic and human resources to be conducted and updated (Cuijpers et al., 2022). Moreover, each new study derived from a MARDs must be properly preregistered to minimize potential biases. Additionally, standardizing and synthesizing information from the included studies may be challenging due to, for example, missing data or heterogeneity between studies. In addition, as with other approaches aimed to synthesize evidence, MARDs cannot be better than the studies included.
This study may have multiple implications for research, clinical practice, and the public. This study could contribute not only to identifying the most effective interventions for preventing perinatal depression, but also to identifying key moderators (e.g., age range, history of depression, perinatal stage, intervention format), thereby advancing toward personalized prevention approaches. The first phase of the study, the MARD, could also help detect gaps in the evidence or areas where additional primary studies are needed, thus guiding future research priorities in perinatal mental health. Moreover, as MARD data will be periodically updated and open access, they will facilitate evidence-based decision-making without the need to conduct new meta-analytic studies (Cuijpers et al., 2022), thereby supporting the development of more precise clinical guidelines.
The inclusion of the PPI in the second phase of the study may further strengthen the societal relevance, accessibility, and usability of the resulting tool (Brett et al., 2014; Canfield et al., 2022; Weirauch et al., 2026). By involving women with lived experience in the refinement process, the project may help ensure that the web-based resource is understandable, acceptable, and useful for end users as well as for clinicians and researchers. In this way, the PPI approach may support more inclusive evidence-informed decision-making and improve access to meta-analytic evidence on preventive interventions for perinatal depression (Brett et al., 2014; Staniszewska et al., 2017). More broadly, this participatory approach is also aligned with current calls to strengthen public engagement and citizen science practices (Van den Bussche et al., 2025) and could empower women to make more informed decisions regarding their mental health during the perinatal period (Schilling & Gerhardus, 2024). In this way, the PPI approach may support more inclusive evidence-informed decision-making and improve access to meta-analytic evidence on preventive non-pharmacological interventions for perinatal depression
Conclusion
Perinatal depression is a major public health issue; however, it remains under-recognized in many settings. In recent years, numerous studies have examined the effectiveness of preventive non-pharmacological interventions for perinatal depression. However, integrating this rapidly expanding body of evidence in a comprehensive, coherent, and updateable way remains challenging. The Metapsy e-Perinatal represents an innovative approach for synthesizing evidence on prevention of perinatal depression, updated periodically, and designed to integrate multiple research questions within a single database. Moreover, the stakeholder-informed refinement of an open-access user-friendly web-based meta-analytic tool for the prevention of perinatal may improve access to up-to-date evidence and support more inclusive evidence-informed decision-making and PPI engagement in research.
Ethics and consent
At phase 1, ethical approval and informed consent will not be required. At phase 2, ethical approval will be obtained, and informed consent will be required from all participants prior to the participation in phase 2.
Generative AI tools statement
The authors used ChatGPT (OpenAI) to assist with English language editing and proofreading of the manuscript.
Acknowledgement
The authors acknowledge the support of the Metapsy e-Perinatal project, funded by the Spanish Ministry of Science and Innovation (Plan Estatal 2021–2023 - Proyectos Investigación Orientada; ID PID2023-149766OB-I00) and the e-Perinatal project, funded by European Research Council (ERC) under the European Union’s Horizon Europe research and innovation programme (grant agreement N° 101042139). Additionally, the authors would like to thank the Metapsy team for their support.
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