# Cluster-Based Evaluation of Dietary Guideline Adherence and Food Literacy Among Adolescents: Implications for Tailored Diets

## TL;DR

This study identifies different dietary patterns among Korean adolescents and shows how food literacy and tailored strategies can improve diet quality.

## Contribution

The paper introduces a cluster-based approach to assess dietary adherence and food literacy, revealing distinct adolescent dietary patterns and their implications for tailored interventions.

## Key findings

- Four distinct dietary clusters were identified, with significant differences in dietary guideline adherence and food literacy.
- Food literacy strongly correlates with dietary guideline adherence and explains 25% of its variance.
- Cluster-specific strategies are recommended to improve diet quality in school- and community-based programs.

## Abstract

Background: Adolescence is a formative period for lifelong dietary patterns, yet Korean adolescents show low fruit and vegetable intake, high sugar and sodium consumption, and rising obesity, highlighting the importance of multidimensional assessment that integrates behavioral, cultural, environmental, and competency-related factors. Methods: A total of 1010 adolescents aged 12–18 years completed an online cross-sectional survey assessing food intake, dietary and physical activity behaviors, dietary culture, and Food Literacy (FL) competencies. Standardized scores were used for hierarchical and K-means clustering to identify dietary practice patterns, and between-cluster differences were examined using ANOVA. Correlation and regression analyses were conducted to examine associations between Dietary Guideline Adherence (DGA) and FL. Results: Four clusters were identified—selective intake–low support (20.4%), regular habits–unbalanced intake (33.3%), high adherence (23.2%), and low adherence (23.1%)—with significant differences in DGA total and domain scores (p < 0.001). The high-adherence cluster showed balanced intake, regular routines, and strong household support, whereas the low-adherence cluster showed poor diet quality, irregular behaviors, and lower socioeconomic status. FL differed across clusters (p < 0.001) and correlated with DGA (r = 0.496, p < 0.01). Total FL predicted DGA (β = 0.496, p < 0.001), explaining 25% of its variance (R2 = 0.246). Conclusions: Adolescent diet quality appears to be associated with behavioral, cultural, and competency-related factors. These findings suggest that cluster-specific strategies—such as fat–sugar–sodium reduction, promotion of low-sodium and diverse diets, and maintenance of balanced-dietary patterns—may support tailored school- and community-based nutrition programs and inform further longitudinal and intervention research.