To explore the potential categories of longitudinal trajectories of patient activation in individuals with chronic heart failure and to analyze their influencing factors.

Patients with chronic heart failure admitted to our hospital from January 2023 to August 2024 were selected as the study subjects.Before discharge(T1),surveys were conducted using a general information questionnaire,the Chinese version of the Patient Activation Measure(PAM13),the FRAIL scale(to screen for frailty risk),and the Social Support Rating Scale(SSRS).Patient activation was reassessed using the Chinese version of the PAM 13 at 1 month(T2),3 months(T3),and 6 months(T4) after discharge.Spearman correlation analysis was used to examine the correlations of patient activation scores across the four time points.Latent class growth modeling was employed to identify potential categories of patient activation trajectories,and multinomial logistic regression was performed to analyze the influencing factors of these trajectory categories.

A total of 216 patients completed the study.The patient activation scores at T1,T2,T3,and T4 were 54.6 (40.1,66.0),56.4(43.4,73.1),51.4(41.7,63.2),and 51.4(42.1,66.0),respectively.Spearman correlation analysis showed positive correlations among patient activation scores at all four time points(all P<0.01).The latent class growth model identified three distinct trajectory categories:high activation-stable group(27.3%),moderate activation-declining group(32.9%),and low activation-gradually increasing group(39.8%).Multinomial Logistic regression revealed that education level,NYHA functional class,comorbidities,frailty status,and social support were significant influencing factors for the trajectory categories of patient activation in chronic heart failure patients(all P<0.05).

There is significant inter-individual heterogeneity in the trajectories of patient activation among chronic heart failure patients,which are influenced by education level,NYHA functional class,comorbidities,frailty status,and social support.Healthcare providers should implement targeted interventions based on the characteristics of patients' activation trajectories to comprehensively improve their activation levels.