ArticleClinical interventions in aging2025
Exploring Self-Management Behavior Profiles in Patients with Multimorbidity: A Sequential, Explanatory Mixed-Methods Study.
Article in Clinical interventions in aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Naturalistic Decision-Making Patterns in Cardiometabolic Multimorbidity Self-Care: A Qualitative Study.Annals of family medicine · 2026Article
- Illness perception and self-management behaviours in adults with multimorbidity: a systematic review protocol.BMJ open · 2026Article
- Clinical Performance and Calibration of the PROFUND Index in Hospitalized and Ambulatory Complex Chronic Patients: A Real-World Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- Pathways to self-management among older adults with multimorbidity in rural China: A fuzzy-set qualitative comparative analysis.International journal of nursing sciences · 2026Article
- Factors Influencing Self-Management Behaviors in Patients with Atopic Dermatitis and Intervention Strategies: A Narrative Review.Patient preference and adherence · 2026Review
- Organizing everyday management in older adults with multimorbidity: a qualitative study from a time-geography perspective.Frontiers in medicine · 2026Article
- Constructing dyadic management behavioral personas for patient-caregiver dyads in ambulatory chemotherapy: A qualitative study.Asia-Pacific journal of oncology nursing · 2025Article
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Authors and funding
12 authors.
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Abstract
Purpose: 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 ( 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.
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