Digital Health Solution for Postpartum Depression

Overseen By Kristen Mackiewicz Seghete, PhD

Age: 18 - 65

Sex: Female

Trial Phase: Academic

Sponsor: Oregon Health and Science University

Disqualifiers: Mindfulness-based intervention, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 1 Jurisdiction This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 4

Duration: 4 weeks

90 Participants Needed

Trial Summary

Do I have to stop taking my current medications for this trial?

The trial protocol does not specify whether you need to stop taking your current medications.

What data supports the idea that Digital Health Solution for Postpartum Depression is an effective treatment?

The available research shows that digital health solutions for postpartum depression, like the MamaLift Plus app, are considered acceptable and practical by users for managing their condition. Although more research is needed, these tools are seen as highly usable. Additionally, the Mothers and Babies Online Course was rated favorably for its usefulness and understanding, indicating that digital tools can effectively engage users. However, the research also highlights that many users do not complete all available lessons, suggesting room for improvement in engagement. Overall, digital health solutions offer a promising alternative for those with limited access to traditional mental health care. ^{1 2 3 4 5}

What safety data exists for the Digital Health Solution for Postpartum Depression?

The provided research does not contain specific safety data for the Digital Health Solution for Postpartum Depression or its other names like Center M or Digital MBCT-PD. The studies focus on general adverse event detection tools, MedWatch data accuracy, postmarketing safety monitoring, and adverse drug events in different settings, but do not mention this specific treatment. ^{6 7 8 9 10}

Is the treatment Center M Digital Health Solution a promising treatment for postpartum depression?

Yes, the Center M Digital Health Solution is promising because digital health tools like it can improve mental health outcomes by making treatment more accessible and engaging for new mothers who might not have easy access to traditional mental health care. ^{1 2 4 11 12}

What is the purpose of this trial?

Center M is a digital health solution to Perinatal Depression (PD) which provides an alternative to Mindfulness Based Cognitive Therapy - Perinatal Depression (MBCT-PD). Center M shifts treatment to a telehealth model, reduces the number of sessions, and transitions home practice materials to a digital format. This study will include a clinical trial in which participants will be randomized to the Center M treatment with smartphone app delivery of homework compared to treatment as usual (TAU).

Research Team

Ellen Tilden, PhD, CNM, FACNM

Principal Investigator

Oregon Health and Science University

Eligibility Criteria

This trial is for pregnant individuals aged 18-50 who speak English and can attend online group meetings. It's designed to help those experiencing depression during pregnancy, including postpartum depression.

Inclusion Criteria

You are currently expecting.

I can attend online meetings as scheduled.

You are proficient in English.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

1-2 weeks

Treatment

Participants receive digital PD screening and four weekly one-hour group telehealth Mindfulness Cognitive Behavioral Therapy sessions with digitally delivered home practice materials

4 weeks

4 visits (virtual)

Follow-up

Participants are monitored for PD symptoms and satisfaction with care post-intervention

6 weeks

Treatment Details

Interventions

• Center M

Trial Overview The study compares a new telehealth program called Center M, which includes an app for homework, with the usual treatment methods for perinatal depression. Participants will be randomly assigned to one of these two options.

Participant Groups

Group I: Center M Experimental Treatment 1 Intervention

This intervention will include digital PD screening as well as four weekly one-hour group telehealth Mindfulness Cognitive Behavioral Therapy sessions and digitally delivered home practice materials for skill building between online group sessions.

Group II: Treatment as Usual (TAU) Active Control 1 Intervention

This arm will receive the current standard of care, which includes a paper-based PD screening and a PD educational handout.

Center M is already approved in United States for the following indications:

Approved in United States as Center M for:

• Perinatal Depression

Who Is Running the Clinical Trial?

Oregon Health and Science University

Lead Sponsor

Trials

1,024

Recruited

7,420,000+

National Institute of Mental Health (NIMH)

Collaborator

Trials

3,007

Recruited

2,852,000+

Findings from Research

The MamaLift Plus app was evaluated by 14 participants over a 2-week period and was found to be an acceptable and highly usable tool for managing postpartum depression (PPD).

Participants indicated that the app is practical for weekly use, suggesting it could be a valuable resource for individuals with limited access to traditional mental health care, highlighting the need for further research in digital behavioral health interventions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Consumer acceptance of using a digital technology to manage postpartum depression. Tang, JJ., Malladi, I., Covington, MT., et al.[2022]

Digital health technologies can significantly enhance the management and screening of peripartum depression (PPD), which affects 10-15% of women in the U.S. annually, by utilizing a tailored framework called Digilego.

The development of specific digital features, such as 'My Diary' and 'Mom Talk', aims to improve patient engagement and address the unique needs of women experiencing PPD, with initial evaluations suggesting these features may provide technological advantages over existing solutions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Digilego for Peripartum Depression: A Novel Patient-Facing Digital Health Instantiation. Zingg, A., Rogith, D., Refuerzo, JS., et al.[2021]

Mobile applications for postpartum depression (PPD) show promise in delivering mental health interventions, but their effectiveness in engaging women and promoting lasting behavioral changes is not well understood, as analyzed in a review of 15 commercial apps.

The study found that while these apps included an average of four behavioral change techniques, there was an inverse relationship between the number of these features and patient engagement, indicating that more features do not necessarily lead to better user involvement.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Mobile Health Applications for Postpartum Depression Management: A Theory-Informed Analysis of Change-Use-Engagement (CUE) Criteria in the Digital Environment. Zingg, A., Carter, L., Rogith, D., et al.[2022]

The Mothers and Babies Online Course (eMB) was designed to prevent postpartum depression (PPD) and was accessed by 208 users, including 37% perinatal women and 63% health providers, highlighting its potential to reach those with limited access to mental health resources.

User feedback indicated that while the lessons were rated positively for usefulness, engagement was low, with only 4.4% of participants completing all eight lessons, suggesting a need for improvements in content delivery to enhance user interaction and effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Mothers and Babies Online Course: Participant Characteristics and Behaviors in a Web-Based Prevention of Postpartum Depression Intervention. Barrera, AZ., Morris, SY., Ruiz, A.[2022]

The mobile application 'Happy Mother' was developed to help manage postpartum depression using principles of cognitive behavioral therapy, making it a potentially effective tool for mothers seeking self-management options without needing to visit a healthcare facility.

Usability testing showed that the app successfully increased awareness of mood and provided practical self-management strategies, indicating its potential to improve mental health outcomes for postpartum women.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Development of the Postpartum Depression Self-Management Mobile Application "Happy Mother". Seo, JM., Kim, SJ., Na, H., et al.[2023]

In a study of 1324 discharged patients, 10.88% experienced adverse events (AEs), with medication-related AEs occurring in 90% of those affected, highlighting the significant risk of harm from neuropsychiatric treatments.

The neuropsychiatry trigger tool (NPTT) was highly effective, identifying 30 times more AEs than traditional reporting methods, suggesting it could be a valuable tool for improving patient safety and reducing harm in hospital settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development of a Trigger Tool to Identify Adverse Events and Harm in a Neuropsychiatry Setting. Sharma, S., Kapoor, K., Nasare, N., et al.[2023]

In 2011, the MedWatch system received 600,000 adverse event reports, highlighting its role in improving drug and device safety awareness, but it also faces challenges with reporting bias and data accuracy.

A study analyzing 10.2 million reports found that while patient information was mostly accurate, suspect product data was often incomplete or incorrect, with significant gaps in key details like dosage and product lot numbers, which could lead to incorrect conclusions about drug safety.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluating the completeness and accuracy of MedWatch data. Getz, KA., Stergiopoulos, S., Kaitin, KI.[2017]

The safety profile of medications can change after they are approved for public use, as new safety issues may arise that were not identified during clinical trials.

Regulatory agencies, like the FDA, actively monitor medication safety through various methods, including analyzing adverse experience reports and using large electronic databases to detect potential safety signals.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Monitoring product safety in the postmarketing environment. Sharrar, RG., Dieck, GS.[2021]

In a pilot study involving 6 academic children's hospitals and 600 patient records, a novel pediatric trigger tool identified 240 harmful events, indicating a significant rate of 40 harms per 100 patients admitted.

Nearly half (45%) of the identified harms were deemed potentially or definitely preventable, highlighting the need for improved safety measures in pediatric inpatient care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A trigger tool to detect harm in pediatric inpatient settings. Stockwell, DC., Bisarya, H., Classen, DC., et al.[2022]

In a study of 25,145 hospital visits, significant adverse drug events (ADEs) were identified, with 8.2% of patients on warfarin and 13.6% on heparin experiencing ADEs, highlighting the risks associated with these high-alert medications.

The presence of ADEs was linked to longer hospital stays and increased mortality rates, with an estimated 888,000 ADEs occurring among hospitalized Medicare patients, indicating a substantial burden on patient safety that could inform future healthcare policies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Adverse drug events among hospitalized Medicare patients: epidemiology and national estimates from a new approach to surveillance. Classen, DC., Jaser, L., Budnitz, DS.[2022]

Postpartum depression (PPD) affects about 1 in 7 women within the first 3 months after childbirth, and if untreated, it can have long-lasting negative effects on both mothers and their children.

The MedEdPPD website was created to equip healthcare professionals with essential tools and resources for effectively screening, diagnosing, and treating PPD, and has seen significant engagement, particularly among nurses, with over 17,000 visitors and positive user feedback since its launch.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Web-based education for postpartum depression: conceptual development and impact. Wisner, KL., Logsdon, MC., Shanahan, BR.[2018]

A machine learning model using Naive Bayes was developed to effectively predict the risk of postpartum depression (PPD) within the first week after childbirth, demonstrating a good balance of sensitivity and specificity.

The model has been integrated into a mobile health application for Android, allowing for quick and easy screening of new mothers, which can facilitate early intervention and support for those at risk of PPD.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning. Jiménez-Serrano, S., Tortajada, S., García-Gómez, JM.[2022]

References

1. Switzerland pubmed.ncbi.nlm.nih.gov

Consumer acceptance of using a digital technology to manage postpartum depression. [2022]

2. United States pubmed.ncbi.nlm.nih.gov

Digilego for Peripartum Depression: A Novel Patient-Facing Digital Health Instantiation. [2021]

3. Netherlands pubmed.ncbi.nlm.nih.gov

Mobile Health Applications for Postpartum Depression Management: A Theory-Informed Analysis of Change-Use-Engagement (CUE) Criteria in the Digital Environment. [2022]

4. Switzerland pubmed.ncbi.nlm.nih.gov

Mothers and Babies Online Course: Participant Characteristics and Behaviors in a Web-Based Prevention of Postpartum Depression Intervention. [2022]

5. United States pubmed.ncbi.nlm.nih.gov

The Development of the Postpartum Depression Self-Management Mobile Application "Happy Mother". [2023]

6. United States pubmed.ncbi.nlm.nih.gov

Development of a Trigger Tool to Identify Adverse Events and Harm in a Neuropsychiatry Setting. [2023]

7. United States pubmed.ncbi.nlm.nih.gov

Evaluating the completeness and accuracy of MedWatch data. [2017]

8. England pubmed.ncbi.nlm.nih.gov

Monitoring product safety in the postmarketing environment. [2021]

9. United States pubmed.ncbi.nlm.nih.gov

A trigger tool to detect harm in pediatric inpatient settings. [2022]

10. Netherlands pubmed.ncbi.nlm.nih.gov

Adverse drug events among hospitalized Medicare patients: epidemiology and national estimates from a new approach to surveillance. [2022]

11. Austria pubmed.ncbi.nlm.nih.gov

Web-based education for postpartum depression: conceptual development and impact. [2018]

12. United States pubmed.ncbi.nlm.nih.gov

A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning. [2022]