The heterogeneity and associated factors of self-management behaviors in patients with type 2 diabetes: a cross-sectional latent profile analysis

Background Type 2 diabetes mellitus (T2DM) poses a growing global health burden, yet patient adherence to self-management behaviors remains suboptimal and highly variable. Traditional analyses assume homogeneity, overlooking distinct behavioral subgroups that may require different interventions. This study aimed to characterize heterogeneous subtypes of diabetes self-management behaviors among adults with T2DM using latent profile analysis (LPA), and explore clinical and psychosocial factors associated with distinct behavioral subgroups to support personalized diabetes care. Methods In this study, 171 adults with type 2 diabetes mellitus (T2DM) were consecutively recruited from a tertiary-care hospital in Guangdong, China, between September 2024 and March 2025. Data collection included validated assessments of general demographics, diabetes self-management behaviors (Summary of Diabetes Self-Care Activities, SDSCA), illness perception (Brief Illness Perception Questionnaire, B-IPQ), diabetes health literacy (Diabetes Health Literacy Scale, DHLS), and medical coping styles (Medical Coping Modes Questionnaire, MCMQ). Latent profile analysis was conducted with Mplus 8.0 to identify distinct self-management behavioral subgroups. Multinomial logistic regression (SPSS 26.0) was used to examine correlates differentiating subgroup membership. Results The average total SDSCA score of participants was 45.43 ± 15.79. LPA identified three interpretable latent profiles of self-management behaviors: Multidimensional Low Self-Management (31.0%, n = 53), Unbalanced Self-Management (13.5%, n = 23), and Comprehensive Optimal Self-Management (55.5%, n = 95). Univariate analyses detected inter-group differences in age, educational attainment, diabetic complications, diabetes duration, HbA1c, illness perception and diabetes health literacy (all p < 0.05). Taking the Comprehensive Optimal Self-Management group as the reference, multinomial logistic regression revealed shared correlates across two suboptimal subgroups: absence of diabetic complications and higher negative illness perception scores. Low health literacy was uniquely correlated with membership in the Multidimensional Low Self-Management profile; older age and low educational attainment (junior high school or below) were uniquely correlated with membership in the Unbalanced Self-Management profile (all p < 0.05). Conclusion Adults with T2DM present prominent heterogeneity in diabetes self-management behaviors and can be divided into three distinct latent subgroups with disparate self-care patterns. Illness perception, complication status, health literacy, age and education level are variables correlated with subgroup allocation (these associations do not indicate causal effects). Targeted, individualized nursing and clinical interventions matching the characteristics of each latent profile are recommended to optimize diabetes self-management and long-term glycemic outcomes. Impact The findings have direct implications for clinical practice by underscoring the necessity of moving beyond a ‘one-size-fits-all’ approach in diabetes care. This study highlights the need to explore the underlying features correlated with heterogeneity in self-management, which could inform the development of more effective, personalized intervention models. Patient or public contribution The patient participants contributed by providing the survey data.