A Chatbot to Engage Parents of Preterm and Term Infants on Parental Stress, Parental Sleep, and Infant Feeding: Usability and Feasibility Study

Jill Wong, Agathe C Foussat, Steven Ting, Enzo Acerbi, Ruurd M van Elburg, Chua Mei Chien, Jill Wong, Agathe C Foussat, Steven Ting, Enzo Acerbi, Ruurd M van Elburg, Chua Mei Chien

Abstract

Background: Parents commonly experience anxiety, worry, and psychological distress in caring for newborn infants, particularly those born preterm. Web-based therapist services may offer greater accessibility and timely psychological support for parents but are nevertheless labor intensive due to their interactive nature. Chatbots that simulate humanlike conversations show promise for such interactive applications.

Objective: The aim of this study is to explore the usability and feasibility of chatbot technology for gathering real-life conversation data on stress, sleep, and infant feeding from parents with newborn infants and to investigate differences between the experiences of parents with preterm and term infants.

Methods: Parents aged ≥21 years with infants aged ≤6 months were enrolled from November 2018 to March 2019. Three chatbot scripts (stress, sleep, feeding) were developed to capture conversations with parents via their mobile devices. Parents completed a chatbot usability questionnaire upon study completion. Responses to closed-ended questions and manually coded open-ended responses were summarized descriptively. Open-ended responses were analyzed using the latent Dirichlet allocation method to uncover semantic topics.

Results: Of 45 enrolled participants (20 preterm, 25 term), 26 completed the study. Parents rated the chatbot as "easy" to use (mean 4.08, SD 0.74; 1=very difficult, 5=very easy) and were "satisfied" (mean 3.81, SD 0.90; 1=very dissatisfied, 5 very satisfied). Of 45 enrolled parents, those with preterm infants reported emotional stress more frequently than did parents of term infants (33 vs 24 occasions). Parents generally reported satisfactory sleep quality. The preterm group reported feeding problems more frequently than did the term group (8 vs 2 occasions). In stress domain conversations, topics linked to "discomfort" and "tiredness" were more prevalent in preterm group conversations, whereas the topic of "positive feelings" occurred more frequently in the term group conversations. Interestingly, feeding-related topics dominated the content of sleep domain conversations, suggesting that frequent or irregular feeding may affect parents' ability to get adequate sleep or rest.

Conclusions: The chatbot was successfully used to collect real-time conversation data on stress, sleep, and infant feeding from a group of 45 parents. In their chatbot conversations, term group parents frequently expressed positive emotions, whereas preterm group parents frequently expressed physical discomfort and tiredness, as well as emotional stress. Overall, parents who completed the study gave positive feedback on their user experience with the chatbot as a tool to express their thoughts and concerns.

Trial registration: ClinicalTrials.gov NCT03630679; https://ichgcp.net/clinical-trials-registry/NCT03630679.

Keywords: anxiety; chatbot; eHealth; infant feeding; parental sleep; parental stress; preterm infants; sleep; stress; support; term infants; usability.

Conflict of interest statement

Conflicts of Interest: ACF was affiliated with Danone Nutricia Research, Precision Nutrition D-lab, Singapore, at the time the work was performed. JW was affiliated with Danone Nutricia Research, Precision Nutrition D-lab, Singapore, at the time the work was performed. ST was affiliated with Danone Nutricia Research, Precision Nutrition D-lab, Singapore, at the time the work was performed and is currently affiliated with Cytel Singapore Private Ltd. EA was affiliated with Danone Nutricia Research, Precision Nutrition D-lab, Singapore, at the time the work was performed and is currently affiliated with NLYTICS Pte. Ltd, Singapore. RMvE was affiliated with Danone Nutricia Research at the time the work was performed and is currently affiliated with Emma Children’s Hospital, Amsterdam University Medical Center, The Netherlands; and Nutrition4Health, Hilversum, The Netherlands. CMC has no conflicts of interest to declare.

©Jill Wong, Agathe C Foussat, Steven Ting, Enzo Acerbi, Ruurd M van Elburg, Chua Mei Chien. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org), 26.10.2021.

Figures

Figure 1
Figure 1
Workflow for conversation data processing and semantic analysis by latent Dirichlet allocation (LDA) topic modeling.
Figure 2
Figure 2
Participant flowchart.
Figure 3
Figure 3
Rating of overall sleep quality by term and preterm group parents.
Figure 4
Figure 4
Three most representative words for each topic learned from conversations in the stress domain.
Figure 5
Figure 5
Three most representative words for each topic learned from conversations in the sleep domain.
Figure 6
Figure 6
Topic prevalence in stress domain conversations from term and preterm group parents.

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