A latent profile analysis of heterogeneity in self-management behavior of gestational diabetes mellitus patients

To identify latent profiles of self-management behaviors among patients with Gestational Diabetes Mellitus (GDM) and develop targeted interventions. s Between July 2023 and October 2023, 320 GDM patients were surveyed using a self-management behavior questionnaire. Latent profile analysis (LPA) was employed to identify subgroups of GDM patients. Subsequent multinomial latent variable regressions were used to identify factors associated with self-management behavior. 23.0%, 47.0%, and 29.9% of respondents were classified into high, moderate, and low self-management groups, respectively, based on the results of the latent profile analysis. The three different categories demonstrated statistically significant differences across scale scores and dimensions (all p < 0.001). The findings showed that age was a predictor of class 2 (OR:0.93,95%CI:0.872–0.994)and was associated with reduced self-management behavior. The higher BIPS(OR:1.03,95%CI:1.007–1.044;OR:1.04,95%CI:1.015–1.057) and QOL(OR:1.05,95%CI:1.028–1.077;OR:1.06,95%CI:1.036–1.092) mean scores were significantly more likely to be in class2 and class3. Patients with a sleep disorder (OR:0.32,95%CI:0.167–0.599; OR:0.27,95%CI:0.130–0.544)were significantly more likely to be class 2 and class 3. Having a blood glucose normal before pregnancy(OR:4.17,95%CI:1.013–17.295) was significantly more likely to be in class 3. The GDM patient population is heterogeneous, with distinct subtypes that may benefit from tailored, multi-level interventions.