ArticleFrontiers in sleep2026
Feasibility and acceptability of Nenne Navi-AI: family-tailored intervention to improve sleep in young Japanese children.
Article in Frontiers in sleep, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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10 authors.
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Abstract
Introduction: 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. Methods: 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. Results: 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. Conclusions: 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.
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