Akademik Veri Yönetim Sistemi
7. ULUSLARARASI KARADENİZ MODERN BİLİMSEL ARAŞTIRMALAR KONGRESİ, Artvin, Türkiye, 24 Haziran - 26 Temmuz 2025, ss.139, (Özet Bildiri)
Introduction and Aim: Nutrition and exercise are fundamental components in maintaining the health of young adults. However, individuals in this age group often face irregular eating patterns and low levels of physical activity due to factors such as a busy lifestyle and stress. This study aimed to identify groups of young adults with similar nutrition and exercise habits using K-means clustering analysis, thereby contributing to the planning of targeted health interventions. Materials and Methods: The study included 250 young adults who completed a 45-item scale measuring various aspects of nutrition and exercise behaviors, including healthy/unhealthy habits, meal regularity, and emotional eating. Data were analyzed using SPSS 26.0. Based on behavioral profiles, participants were divided into three clusters through K-means clustering analysis: Cluster 1 (n = 77), Cluster 2 (n = 39), and Cluster 3 (n = 134). This analysis revealed different patterns in nutrition and exercise behaviors within the sample. Results: The clustering analysis identified three distinct behavioral profiles: Cluster 1 had the lowest scores across all subdimensions, including emotional eating (22.01), unhealthy behaviors (30.48), healthy behaviors (30.81), and meal regularity (12.84), with an overall mean score of 96.14. Cluster 2 had the highest scores in all subdimensions: emotional eating (40.77), unhealthy behaviors (48.95), healthy behaviors (52.67), and meal regularity (18.77), with an overall mean score of 161.15. Cluster 3 showed moderate scores across dimensions, with an overall mean of 126.84. Statistically significant differences were found between the clusters in terms of age and body mass index (BMI) across all subdimensions (p < 0.05). Discussion and Conclusion: There are notable differences in nutrition and exercise behaviors among young adults. While some individuals demonstrate low levels of engagement, others exhibit high but inconsistent behavior patterns. The third group presents a more balanced profile. Identifying these behavioral clusters may contribute to the development of more effective and personalized health interventions for young adults.