Exploring Self-Management Behavior Profiles in Patients with Multimorbidity: A Sequential, Explanatory Mixed-Methods Study - Fu Y; Wu J; Guo Z; Shi Y; Zhao B; Yu J; Chen D; Wu Q; Xue E; Du H; Zhang H; Shao J (Clinical Interventions in Aging)
Yujia Fu,1,2,* Jingjie Wu,3,* Zhiting Guo,4 Yajun Shi,1 Binyu Zhao,1,2 Jianing Yu,1,2 Dandan Chen,3 Qiwei Wu,1,2 Erxu Xue,3 Haoyang Du,3 Huafang Zhang,1 Jing Shao1,2 1Department of Nursing, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, People’s Republic of China; 2School of Nursing and Institute of Nursing Research, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China; 3Department of Nursing, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China; 4Department of Nursing, The Second Affiliated Hospital of Zhejiang University School of Medicine (SAHZU), Hangzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jing Shao, Zhejiang University School of Medicine, 866 Yuhangtang Road, Hangzhou, 310058, People’s Republic of China, Email 11318202@zju.edu.cnPurpose: This study aims to identify self-management behavior profiles in multimorbid patients, and explore how workload, capacity, and their interactions influence these profiles.Patients and Methods: A sequential explanatory mixed-methods design was employed. In the quantitative phase (August 2022 to May 2023), data were collected from 1,920 multimorbid patients across nine healthcare facilities in Zhejiang Province. Latent Profile Analysis (LPA) was used to identify distinct self-management behavior profiles. Multinomial logistic regression was then used to assess the influence of workload and capacity dimensions (independent variables in Model 1), as well as their interaction (independent variables in Model 2), on these profiles (dependent variables in two models). The qualitative phase (May to August 2023) included semi-structured interviews with 16 participants, and the Giorgi analysis method was used for data categorization and coding.Results: Quantitative analysis revealed three self-management behavior profiles: Symptom-driven Profile (8.0%), Passive-engagement Profile (29.5%), and Active-cooperation Profile (62.5%). Compared to the Active-cooperation Profile, both the Symptom-driven and Passive-engagement Profiles were associated with a higher workload (OR > 1, P < 0.05) and lower capacity (OR < 1, P < 0.05). An interaction of the overall workload and capacity showed a synergistic effect in the Passive-engagement Profile (OR = 1.08, 95% CI = 1.03– 1.13, P < 0.05). Qualitative analysis identified six workload themes, and related coping strategies of three self-management behavior profiles. The integrated results highlighted distinct characteristics: Symptom-driven Profile patients exhibited reactive behaviors with limited health awareness, Passive-engagement Profile patients reduced engagement once symptoms stabilized, while Active-cooperation Profile patients proactively managed their conditions.Conclusion: Identifying three distinct self-management behavior profiles and their relationship with workload and capacity provides valuable insights into multimorbid patients’ experiences, emphasizing the need for tailored interventions targeting workload and capacity to improve health outcomes.Keywords: multimorbidity, self-management, workload, capacity, interaction, mixed-methods study