Objective

As global populations age, nutrition management is important for healthy aging. Digital technologies, including mobile applications, web-based platforms, messaging tools, and artificial intelligence (AI)-enabled systems, are used in personalized nutrition interventions. However, evidence on their characteristics, effectiveness, and user experiences remains limited. This scoping review examined technology-based personalized nutrition interventions for older adults.
Methods
This review followed the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. PubMed, Embase, and Web of Science were searched for studies published from January 1, 2010, to June 30, 2026. Among 2,727 records, 2,044 were screened after duplicate removal, 28 full-text articles were assessed, and 13 articles representing 12 studies were included.
Results
Technologies included mobile, messaging, web-based, information and communications technology, tablet-based, and AI-supported systems. Interventions involved dietary recording, personalized feedback, remote counseling, coaching, and self-monitoring. Outcomes included dietary intake and quality, nutritional status, physical function, frailty, cognition, cardiometabolic indicators, engagement, usability, and acceptability. Findings were more consistent for dietary behaviors and individualized nutrition management. Evidence for physical function, frailty, cognition, cardiometabolic health, and quality of life was limited and often based on small or multidomain studies. Familiar interfaces and professional support were accepted, whereas low digital literacy, complex navigation, and limited food databases were barriers.
Conclusion
Digital personalized nutrition interventions may support dietary behavior change and individualized nutrition management in older adults. Larger studies and user-centered systems addressing digital literacy, usability, cultural context, and AI-feedback validation are needed.

Objective

As global life expectancy rises, the focus has shifted from longevity alone to healthy aging. Although dietary models such as the Mediterranean, Dietary Approaches to Stop Hypertension (DASH), Mediterranean-DASH Intervention for Neurodegenerative Delay, and EAT-Lancet diets show benefits for specific health outcomes, their direct application to South Korean populations is limited by differences in dietary patterns and cultural practices. This study aimed to develop nutritional criteria for a South Korean-adapted longevity diet framework. Methods: A multiphase development approach was used, including a narrative review of major dietary models and clinical nutrition guidelines to identify key components of a longevity diet. Macronutrient distribution, food group intake, and nutrient-specific recommendations were synthesized into a structured framework. The EAT-Lancet reference diet was adjusted from 2,400 to 2,000 kcal/ day to reflect energy requirements of South Korean adults. Results: The proposed framework comprises six domain-specific recommendations, including macronutrient targets of 50%–65% carbohydrates, 10%–20% protein, and 15%–30% fat, with a 1:1 animal to plant protein ratio. Food group recommendations were tailored to South Korean dietary patterns. The framework addresses weight management, glycemic control, cardiovascular health, cognitive function, muscle function, and skin health. It emphasizes whole grains, dietary fiber, plant-based proteins, and unsaturated fats, while limiting refined carbohydrates, added sugars, and saturated fats. Conclusion: This study presents evidence-based nutritional criteria for a South Korean-adapted longevity diet framework that integrates disease prevention with functional health support to promote healthy aging.

Citations

• Life-course nutrition strategies for Korean middle-aged adults across biological transitions, nutritional burdens, and a community-linked precision nutrition model: a narrative review
  Yoo Kyoung Park
  Korean Journal of Community Nutrition.2026; 31(3): 215.     CrossRef