ArticleJMIR formative research2021
Delivering Perinatal Health Information via a Voice Interactive App (SMILE): Mixed Methods Feasibility Study.
Article in JMIR formative research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 27 citations in OpenAlex.
- Voice-Activated Cognitive Behavioral Therapy for Insomnia: A Randomized Clinical Trial.JAMA network open · 2024Trial
- Implementation and User Evaluation of the SANGYAN Digital Health Platform to Enhance Knowledge About COVID-19 and Other Health Conditions: Quasi-Experimental Study.JMIR infodemiology · 2026Article
- Educational interventions using digital technologies to promote parental empowerment in situations of vulnerability: a scoping review.Revista da Escola de Enfermagem da U S P · 2026Article
- Wearable sensor data visualization for patient monitoring and management: a Cancer Wellness application in Vietnam.Future science OA · 2025Article
- Podcasts in Mental, Physical, or Combined Health Interventions for Adults: Scoping Review.Journal of medical Internet research · 2025Article
- Chatbot for Social Need Screening and Resource Sharing With Vulnerable Families: Iterative Design and Evaluation Study.JMIR human factors · 2024Article
- Exploring Maternal and Infant Health App Development and Effectiveness Research: Scoping Review.JMIR pediatrics and parenting · 2024Article
- Planting Seeds for the Future: Scoping Review of Child Health Promotion Apps for Parents.JMIR mHealth and uHealth · 2023Article
- Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data.JMIR formative research · 2023Article
- Testing delivery of components of cognitive behavioral therapy for insomnia to breast cancer survivors by smart speaker: a study protocol.BMC medical informatics and decision making · 2022Article
- Digital Prompts to Increase Engagement With the Headspace App and for Stress Regulation Among Parents: Feasibility Study.JMIR formative research · 2022Article
- Reliability of Commercial Voice Assistants' Responses to Health-Related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information.Journal of medical Internet research · 2021Article
- A Medical Translation Assistant for Non-English-Speaking Caregivers of Children With Special Health Care Needs: Proposal for a Scalable and Interoperable Mobile App.JMIR research protocols · 2020Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPerinatal health care is critically important for maternal health outcomes in infants. The United States fares considerably worse than comparable countries for maternal and infant mortality rates. As such, alternative models of care or engagement are warranted. Ubiquitous digital devices and increased use of digital health tools have the potential to extend the reach to women and infants in their everyday lives and make a positive impact on their health outcomes. As voice technology becomes more mainstream, research is prudent to establish evidence-based practice on how to best leverage voice technology to promote maternal-infant health.
objectiveThe aim of this study is to assess the feasibility of using voice technology to support perinatal health and infant care practices.
methodsPerinatal women were recruited from a large Midwest Children's Hospital via hospital email announcements and word of mouth. Owing to the technical aspects of the intervention, participants were required to speak English and use an iPhone. Demographics, patterns of technology use, and technology use specific to perinatal health or self-care practices were assessed at baseline. Next, participants were onboarded and asked to use the intervention, Self-Management Intervention-Life Essentials (SMILE), over the course of 2 weeks. SMILE provided users with perinatal health content delivered through mini podcasts (ranging from 3 to 8 minutes in duration). After each podcast, SMILE prompted users to provide immediate verbal feedback to the content. An exit interview was conducted with participants to gather feedback on the intervention and further explore participants' perceptions of voice technology as a means to support perinatal health in the future.
resultsIn total, 19 pregnant women (17 to 36 weeks pregnant) were consented. Themes identified as important for perinatal health information include establishing routines, expected norms, and realistic expectations and providing key takeaways. Themes identified as important for voice interaction include customization and user preferences, privacy, family and friends, and context and convenience. Qualitative analysis suggested that perinatal health promotion content delivered by voice should be accurate and succinctly delivered and highlight key takeaways. Perinatal health interventions that use voice should provide users with the ability to customize the intervention but also provide opportunities to engage family members, particularly spouses. As a number of women multitasked while the intervention was being deployed, future interventions should leverage the convenience of voice technology while also balancing the influence of user context (eg, timing or ability to listen or talk versus nonvoice interaction with the system).
conclusionsOur findings demonstrate the short-term feasibility of disseminating evidence-based perinatal support via podcasts and curate voice-captured data from perinatal women. However, key areas of improvement have been identified specifically for perinatal interventions leveraging voice technology. Findings contribute to future content, design, and delivery considerations of perinatal digital health interventions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.