Latent profiles of diabetes distress and determinants in type 2 diabetes patients

Type 2 diabetes is a prevalent chronic condition that may pose substantial psychological challenges for patients, particularly in managing long-term glycemic control and its associated complications. Although previous research has examined diabetes distress among individuals with diabetes, studies investigating its latent profiles and associated factors remain scarce. Therefore, this study aimed to identify distinct latent profiles of diabetes distress among patients with type 2 diabetes and explore the factors associated with profile membership. A convenience sampling method was employed to recruit 155 patients with T2D from a tertiary hospital in Heilongjiang Province. Participants completed a general information questionnaire, the Diabetes Distress Scale, and the Self-Regulation Fatigue Scale (SRFS). Latent profile analysis (LPA) was used to categorize diabetes distress, and unordered multinomial logistic regression was applied to identify factors associated with each profile. Diabetes distress was categorized into three latent profiles: low (38.1%), moderate (17.4%), and high (44.5%). The three-class model was identified as the optimal solution based on model fit indices and classification quality, with high entropy (0.905) indicating good separation between profiles. Factors significantly associated with higher distress included being female, older age, more comorbidities, longer duration of diabetes, higher HbA1c levels, and greater self-regulation fatigue (all P < 0.05). LPA identified three distinct profiles of diabetes distress among patients with type 2 diabetes. Targeted interventions for patients with moderate and high levels of distress, focusing on alleviating psychological burden and self-regulation fatigue, may help improve disease management and overall quality of life.