Identifying the heterogeneity of self-advocacy in Chinese patients with breast cancer using latent profile analysis and symptom networks.

Objective: This study aims to identify subgroups of self-advocacy in patients with breast cancer, assess the heterogeneity among different subgroups, and further delineate symptom networks within each subgroup. Methods: A cross-sectional survey was conducted among 320 patients with breast cancer in Wuxi, China, from September 2023 to March 2024, who completed questionnaires about their demographic and clinical characteristics, the M.D. Anderson Symptom Inventory, and the Female Self Advocacy in Cancer Survivorship scale. Latent profile analysis was conducted to identify subgroups of self-advocacy. Multinomial logistic regression was employed to reveal the heterogeneity of each subgroup in demographics and clinical characteristics. Network analysis was performed to unveil the network structure of clinical symptoms within each subgroup. Results: Three subgroups were identified: "Profile 1: low self-advocacy", "Profile 2: moderate self-advocacy", and "Profile 3: high self-advocacy". Compared with patients in Profile 3, those in Profile 1 and Profile 2 showed a higher tendency to have more severe symptoms. Network analysis further revealed that "lack of appetite" emerged as the core symptom in Profile 1, while the core symptom in Profile 2 and Profile 3 was "distress". Conclusions: Patients in different subgroups manifest individualized self-advocacy. The severity of clinical symptoms might serve as an important risk factor for those with low levels of self-advocacy. Conducting symptom networks of diverse subgroups can facilitate tailored symptom management by focusing on core symptoms, thereby enhancing the effectiveness of interventions and improving patients' self-advocacy and overall quality of life.