Feasibility and acceptability of Nenne Navi-AI: family-tailored intervention to improve sleep in young Japanese children.
Despite advancements in sleep medicine, inadequate sleep habits among young children persist. Establishing appropriate sleep habits in early childhood is essential for supporting physical, emotional, and cognitive development. However, scalable and personalized behavioral interventions for caregivers in community settings remain scarce, particularly AI-enabled systems designed for real-world implementation.
This study evaluated adherence, perceived usefulness, and feasibility of Nenne Navi-AI among 50 caregivers recruited in Hirosaki City, Japan, through community health checkups, childcare facilities, and public advertisements. The culturally tailored application integrates supervised machine-learning models with rule-based algorithms to provide personalized guidance and ongoing support for promoting healthier sleep habits.
During the 6-month intervention, only 3 of 50 caregivers (6%) experienced continuous 3-month data-entry lapses, with no withdrawals. Significant pre-post improvements were observed in children's number of awakenings after sleep onset and subjective sleep quality ratings. Subgroup analyses suggested improvements among children with poorer baseline sleep habits (≥0.5 SD worse than the sample mean). Post-intervention assessments confirmed high caregiver acceptability, satisfaction, and reduced parenting stress.
Nenne Navi-AI demonstrates high feasibility with excellent 6-month adherence and favorable usability feedback. The system shows promise for improving early childhood sleep (night-waking), enhances caregiving experiences, reduces negative parenting emotions, and provides a scalable framework for future AI-enabled pediatric sleep interventions.
This study evaluated adherence, perceived usefulness, and feasibility of Nenne Navi-AI among 50 caregivers recruited in Hirosaki City, Japan, through community health checkups, childcare facilities, and public advertisements. The culturally tailored application integrates supervised machine-learning models with rule-based algorithms to provide personalized guidance and ongoing support for promoting healthier sleep habits.
During the 6-month intervention, only 3 of 50 caregivers (6%) experienced continuous 3-month data-entry lapses, with no withdrawals. Significant pre-post improvements were observed in children's number of awakenings after sleep onset and subjective sleep quality ratings. Subgroup analyses suggested improvements among children with poorer baseline sleep habits (≥0.5 SD worse than the sample mean). Post-intervention assessments confirmed high caregiver acceptability, satisfaction, and reduced parenting stress.
Nenne Navi-AI demonstrates high feasibility with excellent 6-month adherence and favorable usability feedback. The system shows promise for improving early childhood sleep (night-waking), enhances caregiving experiences, reduces negative parenting emotions, and provides a scalable framework for future AI-enabled pediatric sleep interventions.
Authors
Yoshizaki Yoshizaki, Saito Saito, Terui Terui, Kawamura Kawamura, Murata Murata, Tanaka Tanaka, Hirata Hirata, Mohri Mohri, Komatani Komatani, Taniike Taniike
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