Technology-Based Approaches for Supporting Perinatal Mental Health

A scoping review of effective health practices for the treatment of birth trauma

BACKGROUND

There is currently no consensus on the most effective health practices to manage or reduce the effects of birth trauma (BT) and childbirth-related posttraumatic stress disorder (CB-PTSD).

AIM

The aim was to map the current literature on effective health practices for BT/CB-PTSD, identify key elements (the what, when and how) important for effective health practices, and highlight gaps in maternity care.

METHODS

A systematic search was conducted across key nursing, allied, and medical databases (MEDLINE, Scopus, PubMed) for key terms related to (1) birth trauma and (2) intervention. Only peer-reviewed, English-language papers published since 2000 were included to ensure the relevance and timeliness of the findings. Following PRISMA-ScR guidelines, 6,347 articles were identified through databases/registers and citation searching. After removing 1,342 duplicates, 5,005 were screened by title and abstract. A further 4,544 were excluded, leaving 461 for full-text screening. Afterf excluding another 433, 28 papers met inclusion for this review.

FINDINGS

The first session delivered early (within the first 72 h of birth) by a clinician (midwife/psychologist/counsellor) significantly reduced BT/CB-PTSD in the short-term. Both trauma-focused and non-trauma-focused were supported at this stage, provided they were structured. If intervention is delayed (weeks to months post-birth), a trauma-focused, multi-session approach is recommended.

DISCUSSION

Early, structured interventions should be considered routine care for women with BT/CB-PTSD, with more intensive, structured, trauma-focused approach for persistent symptoms. The potential role of digital mental health tools is promising, particularly for women in low-resource settings, but requires further research to evaluate feasibility, acceptability, and sustainability.

Effect of technology-supported mindfulness-based interventions for maternal depression: a systematic review and meta-analysis with implementation perspectives for resource-limited settings

BACKGROUND

Maternal depression is pregnancy and childbirth-related depression during pregnancy (prenatal depression (PND)) or after delivery (postpartum depression (PPD)). It is a recognized global public health concern with extensive repercussions adversely affecting women's well-being and the developmental progress of infants. Mindfulness-based interventions (MBIs) have been shown to be effective in maternal depression. Technology-supported MBI could be an effective preventive strategy for maternal depression, especially in low- and middle-income countries (LMICs) where lack of important resources limits the accessibility to standard care. However, the limited available studies assessing the effect of technology-supported MBIs for maternal depression might be insufficient to reach a definitive conclusion. This systematic review aimed to evaluate the pooled estimated effect of technology-supported MBIs for maternal depression, identify available studies, and reveal applicable health technologies with MBIs.

METHOD

This study was conducted according to the PRISMA-P 2020 and the review protocol was registered in PROSPERO; CRD42024537853. The risk of bias was evaluated using the PEDro scale. The meta-analysis was done with R.

RESULT

Data from 18 articles, none from low-income countries (LICs), were included in the systematic review, representing 2,481 participants, 15 studies were included in the meta-analysis. The pooled effect size indicated that technology-supported MBIs had a positive effect on maternal depression (SMD - 0.55, 95% CI [- 0.70; -0.40], p < 0.001). The sub-group analysis showed that this intervention was effective in both PND (SMD = - 0.57, 95% CI [- 0.74; -0.39], p < 0.001) and PPD (SMD - 0.53, 95% CI [- 0.91; -0.15], p = 0.014).

CONCLUSION

Integrating technology-supported MBIs into maternal care is recommended to enhance maternal mental health. However, the lack of trials in LMICs may limit the generalizability and external validity of this finding and it is crucial to conduct further research, in the area to tailor intervention and maximize its effectiveness. Context-specific trial studies are pivotal for successful program adoption.

Digital phenotyping of depression during pregnancy using self-report data

BACKGROUND

Depression is a common pregnancy complication yet is often under-detected and, subsequently, undertreated. Data collected through mobile health tools may be used to support the identification of depression symptoms in pregnancy.

METHODS

An observational cohort study of 2062 pregnancies collected self-reports of patient history, mood, pregnancy-specific symptoms, and written language using a prenatal support app. These app inputs were used to model depression risk in subsequent 30- and 60-day periods throughout pregnancy. A selective inference lasso modeling approach examined the individual and additive value of each type of patient-reported app input.

RESULTS

Depression models ranged in predictive power (AUC value of 0.64-0.83), depending on the type of inputs. The most predictive model included personal history, daily mood, and acute pregnancy-related symptoms (e.g., severe vomiting, cramping). Across models, daily mood was the strongest indicator of depression symptoms in the following month. Models that retained natural language inputs typically improved predictive accuracy and offered insight into the lived context associated with experiencing depression.

LIMITATIONS

Our findings are not generalizable beyond a digitally literate patient population that is self-motivated to report data during pregnancy.

CONCLUSIONS

Simple patient reported data, including sparse language, shared directly via digital tools may support earlier depression symptom identification and a more nuanced understanding of depression context.

Big data and AI for gender equality in health: bias is a big challenge

Artificial intelligence and machine learning are rapidly evolving fields that have the potential to transform women's health by improving diagnostic accuracy, personalizing treatment plans, and building predictive models of disease progression leading to preventive care. Three categories of women's health issues are discussed where machine learning can facilitate accessible, affordable, personalized, and evidence-based healthcare. In this perspective, firstly the promise of big data and machine learning applications in the context of women's health is elaborated. Despite these promises, machine learning applications are not widely adapted in clinical care due to many issues including ethical concerns, patient privacy, informed consent, algorithmic biases, data quality and availability, and education and training of health care professionals. In the medical field, discrimination against women has a long history. Machine learning implicitly carries biases in the data. Thus, despite the fact that machine learning has the potential to improve some aspects of women's health, it can also reinforce sex and gender biases. Advanced machine learning tools blindly integrated without properly understanding and correcting for socio-cultural sex and gender biased practices and policies is therefore unlikely to result in sex and gender equality in health.

Understanding Symptom Self-Monitoring Needs Among Postpartum Black Patients: Qualitative Interview Study

BACKGROUND

Pregnancy-related death is on the rise in the United States, and there are significant disparities in outcomes for Black patients. Most solutions that address pregnancy-related death are hospital based, which rely on patients recognizing symptoms and seeking care from a health system, an area where many Black patients have reported experiencing bias. There is a need for patient-centered solutions that support and encourage postpartum people to seek care for severe symptoms.

OBJECTIVE

We aimed to determine the design needs for a mobile health (mHealth) patient-reported outcomes and decision-support system to assist Black patients in assessing when to seek medical care for severe postpartum symptoms. These findings may also support different perinatal populations and minoritized groups in other clinical settings.

METHODS

We conducted semistructured interviews with 36 participants-15 (42%) obstetric health professionals, 10 (28%) mental health professionals, and 11 (31%) postpartum Black patients. The interview questions included the following: current practices for symptom monitoring, barriers to and facilitators of effective monitoring, and design requirements for an mHealth system that supports monitoring for severe symptoms. Interviews were audio recorded and transcribed. We analyzed transcripts using directed content analysis and the constant comparative process. We adopted a thematic analysis approach, eliciting themes deductively using conceptual frameworks from health behavior and human information processing, while also allowing new themes to inductively arise from the data. Our team involved multiple coders to promote reliability through a consensus process.

RESULTS

Our findings revealed considerations related to relevant symptom inputs for postpartum support, the drivers that may affect symptom processing, and the design needs for symptom self-monitoring and patient decision-support interventions. First, participants viewed both somatic and psychological symptom inputs as important to capture. Second, self-perception; previous experience; sociocultural, financial, environmental, and health systems-level factors were all perceived to impact how patients processed, made decisions about, and acted upon their symptoms. Third, participants provided recommendations for system design that involved allowing for user control and freedom. They also stressed the importance of careful wording of decision-support messages, such that messages that recommend them to seek care convey urgency but do not provoke anxiety. Alternatively, messages that recommend they may not need care should make the patient feel heard and reassured.

CONCLUSIONS

Future solutions for postpartum symptom monitoring should include both somatic and psychological symptoms, which may require combining existing measures to elicit symptoms in a nuanced manner. Solutions should allow for varied, safe interactions to suit individual needs. While mHealth or other apps may not be able to address all the social or financial needs of a person, they may at least provide information, so that patients can easily access other supportive resources.

Improving parental mental health in the perinatal period: A review and analysis of quality improvement initiatives

Parental mental health is an essential sixth vital sign that, when taken into consideration, allows clinicians to improve clinical outcomes for both parents and infants. Although standards exist for screening, referral, and treatment for perinatal mood and anxiety disorders (PMADs), they are not reliably done in practice, and even when addressed, interventions are often minimal in scope. Quality improvement methodology can accelerate the implementation of interventions to address PMADs, but hurdles exist, and systems are not well designed, particularly in pediatric inpatient facilities. In this article, we review the effect of PMADs on parents and their infants and identify quality improvement interventions that can increase screening and referral to treatment of parents experiencing PMADs.

Recentering responsible and explainable artificial intelligence research on patients: implications in perinatal psychiatry

In the setting of underdiagnosed and undertreated perinatal depression (PD), Artificial intelligence (AI) solutions are poised to help predict and treat PD. In the near future, perinatal patients may interact with AI during clinical decision-making, in their patient portals, or through AI-powered chatbots delivering psychotherapy. The increase in potential AI applications has led to discussions regarding responsible AI and explainable AI (XAI). Current discussions of RAI, however, are limited in their consideration of the patient as an active participant with AI. Therefore, we propose a patient-centered, rather than a patient-adjacent, approach to RAI and XAI, that identifies autonomy, beneficence, justice, trust, privacy, and transparency as core concepts to uphold for health professionals and patients. We present empirical evidence that these principles are strongly valued by patients. We further suggest possible design solutions that uphold these principles and acknowledge the pressing need for further research about practical applications to uphold these principles.

Navigating the Intersection of Technology and Depression Precision Medicine

This chapter primarily focuses on the progress in depression precision medicine with specific emphasis on the integrative approaches that include artificial intelligence and other data, tools, and technologies. After the description of the concept of precision medicine and a comparative introduction to depression precision medicine with cancer and epilepsy, new avenues of depression precision medicine derived from integrated artificial intelligence and other sources will be presented. Additionally, less advanced areas, such as comorbidity between depression and cancer, will be examined.

Yourtime: The development and pilot of a perinatal mental wellbeing digital tool using a co-design approach

INTRODUCTION

Maternal anxiety and depression are major public health issues with prevalence as high as one in five women. There is a need to focus on preventative strategies to enable women to self-monitor their mental health status during pregnancy and postnatally.

AIM

To co-design and test a perinatal mental health digital tool to enable women to self-monitor their mental wellbeing during pregnancy and early parenting and promote positive self-care strategies.

METHODS AND ETHICS

A sequential mixed methods study utilising two stages 1) co-design workshops; 2) fit for purpose pilot with women through a purpose designed survey to evaluate acceptability, useability, functionality, and satisfaction.

FINDINGS

Mothers, midwives, design researchers and students, participated in co-designing a digital tool and prototype application, YourTime. Fourteen participants engaged in the pilot, with all women agreeing that the tool would be beneficial in alerting them to changes in mental wellbeing. Seventy-seven percent agreed that this prototype had the potential to positively affect wellbeing during the perinatal period.

DISCUSSION

The need to develop a perinatal mental health digital tool that enables women to self-monitor their wellbeing was identified. Women reported the YourTime app offered an acceptable and effective means to self-assess and monitor their wellbeing.

CONCLUSION

The YourTime app responds to the growing agenda for digital approaches to address perinatal mental health challenges. The pilot study demonstrated that the app offered potential to alert women to changes in mental wellbeing, but functionality need further development.

Postpartum Mood Disorders: Insights into Diagnosis, Prevention, and Treatment

Postpartum mood disorders pose significant challenges to women's mental health and well-being during the postpartum period. This review article provides insights into these disorders' diagnosis, prevention, and treatment. The article begins by discussing the background information on postpartum mood disorders, their significance, and the purpose of understanding them. It then delves into the classification and types of postpartum mood disorders, emphasizing the need for accurate diagnosis and differentiation. Prevalence and incidence rates are explored to highlight the scope and impact of these disorders. The review examines various risk factors associated with postpartum mood disorders, including biological, psychological, and socioeconomic factors. Understanding these risk factors helps identify high-risk populations and guide targeted interventions. Screening and diagnosis of postpartum mood disorders are crucial for early detection and intervention. The article provides an overview of screening tools, highlights the challenges in diagnosis, and emphasizes the importance of early identification for better outcomes. Prevention strategies are explored, including antenatal education, psychosocial support programs, and the role of healthcare professionals in promoting preventive measures. Effective prevention interventions and their outcomes are discussed to guide healthcare providers and policymakers in implementing evidence-based strategies. Treatment approaches for postpartum mood disorders include pharmacological interventions, psychotherapy options, alternative and complementary therapies, and multidisciplinary approaches. The article discusses the effectiveness and considerations of each approach, highlighting the importance of individualized care. Challenges and barriers in diagnosing, preventing, and treating postpartum mood disorders are addressed, including stigma, limited access to healthcare services, and gaps in healthcare provider knowledge and training. Recommendations are provided for healthcare professionals and policymakers to overcome these challenges and improve outcomes. The review concludes by highlighting the need for future research, innovations in prevention and treatment approaches, and collaborative efforts in the field of postpartum mood disorders. Promising areas for research are identified, including long-term outcomes, understanding risk factors, and cultural considerations. The article emphasizes the importance of interdisciplinary collaboration and stakeholder engagement in advancing the field.

Understanding the impact of an AI-enabled conversational agent mobile app on users' mental health and wellbeing with a self-reported maternal event: a mixed method real-world data mHealth study

Background

Maternal mental health care is variable and with limited accessibility. Artificial intelligence (AI) conversational agents (CAs) could potentially play an important role in supporting maternal mental health and wellbeing. Our study examined data from real-world users who self-reported a maternal event while engaging with a digital mental health and wellbeing AI-enabled CA app (Wysa) for emotional support. The study evaluated app effectiveness by comparing changes in self-reported depressive symptoms between a higher engaged group of users and a lower engaged group of users and derived qualitative insights into the behaviors exhibited among higher engaged maternal event users based on their conversations with the AI CA.

Methods

Real-world anonymised data from users who reported going through a maternal event during their conversation with the app was analyzed. For the first objective, users who completed two PHQ-9 self-reported assessments (n = 51) were grouped as either higher engaged users (n = 28) or lower engaged users (n = 23) based on their number of active session-days with the CA between two screenings. A non-parametric Mann-Whitney test (M-W) and non-parametric Common Language effect size was used to evaluate group differences in self-reported depressive symptoms. For the second objective, a Braun and Clarke thematic analysis was used to identify engagement behavior with the CA for the top quartile of higher engaged users (n = 10 of 51). Feedback on the app and demographic information was also explored.

Results

Results revealed a significant reduction in self-reported depressive symptoms among the higher engaged user group compared to lower engaged user group (M-W p = .004) with a high effect size (CL = 0.736). Furthermore, the top themes that emerged from the qualitative analysis revealed users expressed concerns, hopes, need for support, reframing their thoughts and expressing their victories and gratitude.

Conclusion

These findings provide preliminary evidence of the effectiveness and engagement and comfort of using this AI-based emotionally intelligent mobile app to support mental health and wellbeing across a range of maternal events and experiences.