Using an AI-powered Mobile Application Chatbot to Address Maternal Depression Indicators and Inquiries in the Perinatal and Postpartum Periods: A Multimethod Analysis.
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| Title: | Using an AI-powered Mobile Application Chatbot to Address Maternal Depression Indicators and Inquiries in the Perinatal and Postpartum Periods: A Multimethod Analysis. |
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| Authors: | Peters, Carson J.1 (AUTHOR) cpeters9@umd.edu, Aldana Lainez, Valerie1 (AUTHOR), Clark, Kaili1 (AUTHOR), Jasczynski, Michelle1 (AUTHOR), Nguyen, Quynh C.2 (AUTHOR), Norell, Elizabeth M.1 (AUTHOR) |
| Source: | Inquiry (00469580). 1/29/2026, Vol. 63, p1-10. 10p. |
| Subject Terms: | *Emotion regulation, *Fear, *Focus groups, *Artificial intelligence, *Content analysis, *Confidence, *Information resources, *Parenting, *Anxiety, *Research methodology, *Quality of life, *Health education, *Access to information, *Information-seeking behavior, Mobile apps, Social constructionism, African Americans, Research funding, Mental health, Secondary analysis, Health, Questionnaires, Postpartum depression, Descriptive statistics, Symptom burden, Surveys, Thematic analysis, Psychology of mothers, Health equity, User-centered system design, Data analysis software, Sociodemographic factors, Sleep quality, Social support, Perinatal period, Chatbots, Social stigma, Psychosocial factors |
| Geographic Terms: | United States |
| Abstract: | Using AI-powered mobile applications for mental health screening can help reduce maternal mental health disparities among Black mothers who are pregnant or parenting in the United States. A maternal health education question and answer mobile application chatbot has the potential to intervene in the maternal depression cascade, specifically screening. Extant research demonstrates the usability of mobile applications addressing mental health. However, limited scholarship explores the intersection between AI-powered mobile application chatbots and maternal mental health. This study uses a multimethod analysis to evaluate the usability of an AI-powered mobile application to address maternal mental health among Black women. Data sources, including mobile application engagement, mental health disorder scales, and secondary qualitative analysis from focus group discussions (n = 5), will be assessed through a multimethod approach. The study team previously collected data across the United States for this clinical intervention in 2022. Findings indicate that the mobile application demonstrated promise in the application's usability to screen for maternal health depression indicators. This was achieved using the mobile application's intent classification functionality that classified users' questions that contained targeted search terms (e.g., postpartum depression) or specific inquiries about mental health and appropriate follow-up from the study team to provide mental health resources. Critical interconnected themes were assessed and reflected high confidence, acceptance, and usability of the mobile application in addressing maternal mental health inquiries. Findings contribute to evidence about the usability of AI-powered mobile applications informed by Black mothers in appropriate screening for maternal depression indicators and inquiries. This study provides insight into closing the gap in maternal health disparities in depression outcomes for Black mothers. Trial Registration: ClinicalTrials.gov NCT06053515; https://clinicaltrials.gov/study/NCT06053515. [ABSTRACT FROM AUTHOR] |
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| Database: | Education Research Complete |
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| Abstract: | Using AI-powered mobile applications for mental health screening can help reduce maternal mental health disparities among Black mothers who are pregnant or parenting in the United States. A maternal health education question and answer mobile application chatbot has the potential to intervene in the maternal depression cascade, specifically screening. Extant research demonstrates the usability of mobile applications addressing mental health. However, limited scholarship explores the intersection between AI-powered mobile application chatbots and maternal mental health. This study uses a multimethod analysis to evaluate the usability of an AI-powered mobile application to address maternal mental health among Black women. Data sources, including mobile application engagement, mental health disorder scales, and secondary qualitative analysis from focus group discussions (n = 5), will be assessed through a multimethod approach. The study team previously collected data across the United States for this clinical intervention in 2022. Findings indicate that the mobile application demonstrated promise in the application's usability to screen for maternal health depression indicators. This was achieved using the mobile application's intent classification functionality that classified users' questions that contained targeted search terms (e.g., postpartum depression) or specific inquiries about mental health and appropriate follow-up from the study team to provide mental health resources. Critical interconnected themes were assessed and reflected high confidence, acceptance, and usability of the mobile application in addressing maternal mental health inquiries. Findings contribute to evidence about the usability of AI-powered mobile applications informed by Black mothers in appropriate screening for maternal depression indicators and inquiries. This study provides insight into closing the gap in maternal health disparities in depression outcomes for Black mothers. Trial Registration: ClinicalTrials.gov NCT06053515; https://clinicaltrials.gov/study/NCT06053515. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00469580 |
| DOI: | 10.1177/00469580261417580 |
