OBJECTIVE: To explore latent profiles of self-management behaviors in older adult patients with chronic diseases and identify the factors that influence different profiles, guiding targeted interventions. METHODS: This study used convenience sampling to recruit 536 older adult patients with chronic diseases from three tertiary hospitals in Anhui Province between October 2023 and May 2024. Data were collected using a general information questionnaire, the age-adjusted Charlson Comorbidity Index (aCCI), the Chronic Disease Self-Management Behavior Scale, the Chronic Disease Management Self-Efficacy Scale, the Psychological Status Scale, the Digital Health Literacy Scale, and the Social Support Scale. Latent profile analysis was conducted using Mplus 8.3, and univariate and multivariate logistic regression analyses were performed using SPSS 26.0. RESULTS: Three profiles of self-management behaviors emerged: "Low Self-Management" (50.2%), "High Exercise and Cognitive Management" (8.6%), and "Moderate Management with Enhanced Communication" (41.2%). Multivariate logistic regression revealed that residence, aCCI, number of digital devices used, perceived usefulness of digital health information, digital health literacy, social support, chronic disease management self-efficacy, and psychological status were significant factors affecting self-management profiles (all CONCLUSION: Self-management behaviors in older adult patients with chronic diseases were generally low, with substantial heterogeneity across profiles. Healthcare providers should tailor interventions based on the characteristics of each group to enhance self-management in digital health contexts.