Latent Profiles and Associated Factors of Health Problem-Solving Ability in Patients with Coronary Heart Disease: A Cross-Sectional Study

Purpose: To explore the latent profile characteristics of health problem-solving ability in coronary heart disease (CHD) patients and analyze their demographic and clinical determinants, providing empirical evidence for tailored transitional care. Patients and Methods: A cross-sectional study was conducted among 381 hospitalized CHD patients recruited via convenience sampling from the cardiovascular department of a tertiary hospital in Ganzhou, Jiangxi Province, China, between November 2025 and March 2026. Data were collected using a sociodemographic questionnaire, Health Problem-Solving Scale (HPSS), Cardiac Self-Efficacy Scale (CSES), Coronary Artery Disease Education Questionnaire Short Version (CADE-Q SV), and Perceived Social Support Scale (PSSS). Subgroups were identified via LPA using Mplus 8.3. Multinomial logistic regression explored associated factors using SPSS 27.0. Results: The mean HPSS score was 13.65 ± 4.84. LPA identified three distinct profiles: Rational-Active Coping (26.8%), Ambivalent-Passive Adaptation (52.2%), and Negative-Impulsive Avoidance (21.0%). Compared to the Rational-Active Coping profile, higher perceived social support (OR=0.897) and self-efficacy (OR=0.825) were independent protective factors against the Ambivalent-Passive Adaptation profile (both P < 0.01). Furthermore, poorer disease knowledge (OR=0.796), lower perceived social support (OR=0.785), and diminished self-efficacy (OR=0.681) emerged as significant independent risk factors for the Negative-Impulsive Avoidance profile. Conclusion: CHD patients exhibit significant heterogeneity in health problem-solving ability. Profiling these patients provides a practical framework for personalized clinical management. Healthcare providers could target the “Negative-Impulsive” and “Ambivalent-Passive” groups. Enhancing disease knowledge, social support, and self-efficacy can promote active problem-solving, thereby optimizing self-management and long-term clinical outcomes.