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] |
| Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 191254455 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Using an AI-powered Mobile Application Chatbot to Address Maternal Depression Indicators and Inquiries in the Perinatal and Postpartum Periods: A Multimethod Analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Peters%2C+Carson+J%2E%22">Peters, Carson J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cpeters9@umd.edu</i><br /><searchLink fieldCode="AR" term="%22Aldana+Lainez%2C+Valerie%22">Aldana Lainez, Valerie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Clark%2C+Kaili%22">Clark, Kaili</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jasczynski%2C+Michelle%22">Jasczynski, Michelle</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Quynh+C%2E%22">Nguyen, Quynh C.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" 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Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/00469580261417580 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Subjects: – SubjectFull: Emotion regulation Type: general – SubjectFull: Fear Type: general – SubjectFull: Focus groups Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Content analysis Type: general – SubjectFull: Confidence Type: general – SubjectFull: Information resources Type: general – SubjectFull: Parenting Type: general – SubjectFull: Anxiety Type: general – SubjectFull: Research methodology Type: general – SubjectFull: Quality of life Type: general – SubjectFull: Health education Type: general – SubjectFull: Access to information Type: general – SubjectFull: Information-seeking behavior Type: general – SubjectFull: Mobile apps Type: general – SubjectFull: Social constructionism Type: general – SubjectFull: African Americans Type: general – SubjectFull: Research funding Type: general – SubjectFull: Mental health Type: general – SubjectFull: Secondary analysis Type: general – SubjectFull: Health Type: general – SubjectFull: Questionnaires Type: general – SubjectFull: Postpartum depression Type: general – SubjectFull: Descriptive statistics Type: general – SubjectFull: Symptom burden Type: general – SubjectFull: Surveys Type: general – SubjectFull: Thematic analysis Type: general – SubjectFull: Psychology of mothers Type: general – SubjectFull: Health equity Type: general – SubjectFull: User-centered system design Type: general – SubjectFull: Data analysis software Type: general – SubjectFull: Sociodemographic factors Type: general – SubjectFull: Sleep quality Type: general – SubjectFull: Social support Type: general – SubjectFull: Perinatal period Type: general – SubjectFull: Chatbots Type: general – SubjectFull: Social stigma Type: general – SubjectFull: Psychosocial factors Type: general – SubjectFull: United States Type: general Titles: – TitleFull: Using an AI-powered Mobile Application Chatbot to Address Maternal Depression Indicators and Inquiries in the Perinatal and Postpartum Periods: A Multimethod Analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Peters, Carson J. – PersonEntity: Name: NameFull: Aldana Lainez, Valerie – PersonEntity: Name: NameFull: Clark, Kaili – PersonEntity: Name: NameFull: Jasczynski, Michelle – PersonEntity: Name: NameFull: Nguyen, Quynh C. – PersonEntity: Name: NameFull: Norell, Elizabeth M. IsPartOfRelationships: – BibEntity: Dates: – D: 29 M: 01 Text: 1/29/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00469580 Numbering: – Type: volume Value: 63 Titles: – TitleFull: Inquiry (00469580) Type: main |
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