ArticleJMIR formative research2024
A Chatbot (Juno) Prototype to Deploy a Behavioral Activation Intervention to Pregnant Women: Qualitative Evaluation Using a Multiple Case Study.
Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cocreation and codesign in maternal health: processes, outcomes and implementation challenges.BMJ quality & safety · 2026Pooled it
- The application of chatbots in perinatal mental health interventions: a scoping review protocol.BMJ open · 2026Article
- Designing conversational intelligence: effect of large language models (GPT-driven) platforms for precision maternal and newborn health engagement: a systematic review.Oxford open digital health · 2026Review
- Article
- Charting the evolution of artificial intelligence mental health chatbots from rule-based systems to large language models: a systematic review.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2025Article
- Large language models and women's health: a digital companion for informed decision-making.Archives of gynecology and obstetrics · 2025Article
- Chatbot to Support the Mental Health Needs of Pregnant and Postpartum Women (Moment for Parents): Design and Pilot Study.JMIR formative research · 2025Article
- Navigating promise and perils: applying artificial intelligence to the perinatal mental health care cascade.npj health systems · 2025Review
- Role of Chatbots on Supporting Women in Perinatal Period: A Scoping Review.Women's health reports (New Rochelle, N.Y.)Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite the increasing focus on perinatal care, preventive digital interventions are still scarce. Furthermore, the literature suggests that the design and development of these interventions are mainly conducted through a top-down approach that limitedly accounts for direct end user perspectives.
objectiveBuilding from a previous co-design study, this study aimed to qualitatively evaluate pregnant women's experiences with a chatbot (Juno) prototype designed to deploy a preventive behavioral activation intervention.
methodsUsing a multiple-case study design, the research aims to uncover similarities and differences in participants' perceptions of the chatbot while also exploring women's desires for improvement and technological advancements in chatbot-based interventions in perinatal mental health. Five pregnant women interacted weekly with the chatbot, operationalized in Telegram, following a 6-week intervention. Self-report questionnaires were administered at baseline and postintervention time points. About 10-14 days after concluding interactions with Juno, women participated in a semistructured interview focused on (1) their personal experience with Juno, (2) user experience and user engagement, and (3) their opinions on future technological advancements. Interview transcripts, comprising 15 questions, were qualitatively evaluated and compared. Finally, a text-mining analysis of transcripts was performed.
resultsSimilarities and differences have emerged regarding women's experiences with Juno, appreciating its esthetic but highlighting technical issues and desiring clearer guidance. They found the content useful and pertinent to pregnancy but differed on when they deemed it most helpful. Women expressed interest in receiving increasingly personalized responses and in future integration with existing health care systems for better support. Accordingly, they generally viewed Juno as an effective momentary support but emphasized the need for human interaction in mental health care, particularly if increasingly personalized. Further concerns included overreliance on chatbots when seeking psychological support and the importance of clearly educating users on the chatbot's limitations.
conclusionsOverall, the results highlighted both the positive aspects and the shortcomings of the chatbot-based intervention, providing insight into its refinement and future developments. However, women stressed the need to balance technological support with human interactions, particularly when the intervention involves beyond preventive mental health context, to favor a greater and more reliable monitoring.
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