Tailored Nutritional Intervention Based on AI-Assessed Dietary Assessment in Nutritionally Compromised Older Adults: Impact on Anthropometric, Cognitive, and EEG Parameters

Objectives: The aim of this study was to evaluate whether dietary intake-guided precision nutrition interventions, derived from AI-driven food intake monitoring, improve cognitive function and electroencephalographic (EEG) biomarkers in older adults receiving long-term care. Methods: A total of 108 adults aged ≥50 years were recruited from five long-term care facilities. The study included 4 weeks of AI-driven dietary data collection, 2 weeks of data analysis and participant grouping, and 4 weeks of targeted nutritional intervention. Dietary intake was assessed using an AI-based food scanner. Anthropometric measures, biochemical markers, nutritional and cognitive questionnaires, and EEG were evaluated at baseline and postintervention. Participants were classified into three groups based on nutrient intake: “severely inadequate,” “marginally inadequate,” and “adequate.” Tailored food-based interventions, including nut mixes, senior-friendly meat products, and oral nutritional supplements, were provided according to group classification. Results: Energy intake increased in all groups. Nutritional status and cognitive function improved primarily in the severely inadequate group, while favorable EEG changes were observed across all groups. Changes in dietary composition and lipid profiles were observed in the “adequate” group, whereas the “marginally inadequate” group showed no significant changes. Conclusions: AI-based monitoring and nutritional intervention may improve nutritional status, cognitive-related outcomes, and EEG biomarkers in older adults receiving long-term care, supporting the potential of precision nutrition approaches in this population. Clinical Trial Registration KCT0009558 (CRIS, Republic of Korea).