Development and Preliminary Feasibility Evaluation of a Health Chatbot to Enhance Maternal Behaviors for Developmental Promotion in High-risk Infants: A Pilot Study
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Abstract
Objective: This study aimed to develop and conduct a preliminary feasibility evaluation of a health chatbot designed to enhance maternal behaviors in promoting high-risk infant development. Methods: This developmental research consisted of two phases. Phase 1 involved chatbot development, including content development, conceptual framework and conversational flow design based on Bandura's Social Cognitive Theory, system and interaction design, multimedia development, and content validation by experts. Phase 2 was a pilot study to evaluate the chatbot's preliminary feasibility. Five mothers of at-risk infants used the chatbot for 7 days. Feasibility was assessed in three domains: system functionality, user engagement, and preliminary outcomes. Maternal behaviors for promoting at-risk infant development were assessed using the Maternal Behavior for Promoting At-Risk Infant Development Questionnaire (CVI = .86, α = .97), and infant development was evaluated at 2 months of age using a standardized developmental assessment. Results: The developed chatbot delivered health information and guidance through text, infographics, and videos, based on social cognitive theory to enhance maternal self-efficacy. System testing demonstrated that the chatbot functioned as intended with satisfactory stability. In the pilot phase, all participants completed the 7-day program, although one participant reported unstable internet connectivity. Following the intervention, all mothers demonstrated engagement in developmental promotion behaviors across all five domains at least three days per week. Regarding preliminary developmental outcomes, three infants achieved age-appropriate development in all five domains, while the remaining infants achieved age-appropriate development in four and three domains, respectively. Conclusion: The development and preliminary feasibility evaluation demonstrated that the health chatbot was feasible for use in promoting maternal behaviors that support the development of at-risk infants. The chatbot showed satisfactory system functionality, user engagement, and promising preliminary outcomes. However, further research with larger sample sizes is needed to confirm feasibility and evaluate the long-term effectiveness of the chatbot.
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References
Ministry of Public Health. Manual for assessment and promotion of development in at-risk children.Nonthaburi: Veteran Organization Printing House; 2019.
Department of Health. Summary of the implementation of the developmental promotion project in honor of Her Royal Highness Princess Maha Chakri Sirindhorn on the occasion of her 5th cycle birthday anniversary, April 2, 2015. Bangkok: Veterans Organization Printing Office; 2018.
Tangviriyapaiboon D, Thaineua V, Sirithongthaworn S, et al. Factors associated with suspected developmental delay in Thai children born with low birth weight or asphyxia. Matern Child Health J.2024; 28(4): 631-40. doi: 10.1007/s10995-023-03814-1.
Chamnongsak C. Policy Evaluation: A Case Study of the Child Development Promotion Project in Honor of Princess Maha Chakri Sirindhorn on Her 5th Cycle Birthday, 2 April 2015. J Med Region 4-5. 2017;36(3): 176–87. Thai.
Coughlan S, Quigley J, Nixon E. Parent-infant conversations are differentially associated with the development of preterm- and term-born infants. J Exp Child Psychol. 2024; 239: 105809. doi:10.1016/j.jecp.2023.105809.
Synnes A, Luu TM, Afifi J, et al. Parent-Integrated Interventions to Improve Language Development in Children Born Very Preterm. Children (Basel). 2023; 10(6): 953. doi: 10.3390/children10060953.
Larsson J, Nyborg L, Psouni E. The role of family function and triadic interaction on preterm child development: a systematic review. Children (Basel). 2022; 9(11): 1695. doi: 10.3390/children9111695.
Department of Health Service Support. Manual for chatbot use in the health service system. Nonthaburi: Ministry of Public Health; 2022.
Nukulsompratthana P. Thailand digital and social media statistics 2025 [Internet]. Popticles; 2025 [cited 2026 Jul 13]. Available from: https://www.popticles.com/trends/thailand-digital-and-social-media-2025/
Aggarwal A, Tam CC, Wu D, et al. Artificial intelligence-based chatbots for promoting health behavioral changes: systematic review. J Med Internet Res. 2023; 25: e40789. doi: 10.2196/40789.
Pupong K, Hunsrisakhun J, Pithpornchaiyakul S, et al. Development of chatbot-based oral health care for young children and evaluation of its effectiveness, usability, and acceptability: mixed methods study. JMIR Pediatr Parent. 2025; 8: e62738. doi: 10.2196/62738.
Hunsrisakhun J, Naorungroj S, Tangkuptanon W, et al. Impact of oral health chatbot with and without toothbrushing training on childhood caries. Int Dent J. 2025; 75(2): 1348-1359. doi: 10.1016/j.identj.2024.09.028.
Bandura A. Social foundations of thought and action: A social cognitive theory. New Jersey: Prentice-Hall;1986.
Landauer TK, Nielsen J. A mathematical model of the finding of usability problems. In: Proceedings of the INTERCHI '93 Conference on Human Factors in Computing Systems; 1993 Apr 24-29; Amsterdam, The Netherlands. New York: ACM; 1993. p. 206-13.
Adamopoulou E, Moussiades L. An overview of chatbot technology. In: Maglogiannis I, Iliadis L, Pimenidis E, editors. Artificial intelligence applications and innovations. Cham: Springer; 2020. p.373-83.
Følstad A, Brandtzæg PB. Chatbots and the new world of HCI. Interactions. 2017; 24(4): 38-42. doi: 10.1145/3085558.
Pressman RS, Maxim BR. Software engineering: a practitioner’s approach. 9th ed. New York: McGraw-Hill; 2020.
Nualtem P, Wattanasit P, Kala S. Development and preliminary evaluation of a health chatbot for breast milk expression promotion in mothers of preterm infants admitted to the neonatal intensive care unit. JRN-MHS [Internet]. 2025 [cited 2026 Jul 13]; 45(3): 16-30. Available from: https://he02.tcithaijo.org/index.php/nurpsu/article/view/274544
Kerimoglu Yildiz G, Turk Delibalta R, Coktay Z. Artificial intelligence-assisted chatbot: impact on breastfeeding outcomes and maternal anxiety. BMC Pregnancy Childbirth. 2025; 25(1) :631. doi: 10.1186/s12884-025-07753-3.
Smutny P, Schreiberova P. Chatbots for learning: a review of educational chatbots for the Facebook Messenger. Comput Educ. 2020; 151: 103862. doi: 10.1016/j.compedu.2020.103862.
Provoost S, Lau HM, Ruwaard J, et al. Embodied conversational agents in clinical psychology: a scoping review. J Med Internet Res. 2017; 19(5): e151. doi: 10.2196/jmir.6553.
Perski O, Blandford A, West R, et al. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. 2017; 7(2): 254-67. doi: 10.1007/s13142-016-0453-1.
Laranjo L, Dunn AG, Tong HL, et al. Conversational agents in healthcare: a systematic review. J Am Med Inform Assoc. 2018; 25(9): 1248-58. doi: 10.1093/jamia/ocy072.
Bibault JE, Chaix B, Nectoux P, et al. Healthcare ex machina: are conversational agents ready for prime time in oncology? Clin Transl Radiat Oncol. 2019; 16: 55-59. doi: 10.1016/j.ctro.2019.04.002.
Mayer RE. Evidence-based principles for how to design effective instructional videos. J Appl Res Mem Cogn. 2021; 10(2): 229-40. doi: 10.1016/j.jarmac.2021.03.007.
Yang Y, Tavares J, Oliveira T. A new research model for artificial intelligence–based well-being chatbot engagement: survey study. JMIR Hum Factors. 2024; 11: e59908. doi: 10.2196/59908.
Bickmore TW, Schulman D, Yin L. Maintaining engagement in long-term interventions with relational agents. Appl Artif Intell. 2010; 24(6): 648-66. doi: 10.1080/08839514.2010.492259.
Milne-Ives M, de Cock C, Lim E, et al. The effectiveness of artificial intelligence conversational agents in health care: systematic review. J Med Internet Res. 2020; 22(10): e20346. doi: 10.2196/20346.
Fadhil A, Gabrielli S. Addressing challenges in promoting healthy lifestyles: the AI-chatbot approach. In: Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth 2017); 2017 May 23-26; Barcelona, Spain. New York: ACM; 2017. p. 261-65. doi:10.1145/3154862.3154914.