{"title":"探索多重疾病患者的自我管理行为概况：一项顺序的、解释性的混合方法研究。","authors":"Yujia Fu, Jingjie Wu, Zhiting Guo, Yajun Shi, Binyu Zhao, Jianing Yu, Dandan Chen, Qiwei Wu, Erxu Xue, Haoyang Du, Huafang Zhang, Jing Shao","doi":"10.2147/CIA.S488890","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to identify self-management behavior profiles in multimorbid patients, and explore how workload, capacity, and their interactions influence these profiles.</p><p><strong>Patients and methods: </strong>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.</p><p><strong>Results: </strong>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 (<i>OR</i> > 1, <i>P</i> < 0.05) and lower capacity (<i>OR</i> < 1, <i>P</i> < 0.05). An interaction of the overall workload and capacity showed a synergistic effect in the Passive-engagement Profile (<i>OR</i> = 1.08, 95% <i>CI</i> = 1.03-1.13, <i>P</i> < 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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":48841,"journal":{"name":"Clinical Interventions in Aging","volume":"20 ","pages":"1-17"},"PeriodicalIF":3.5000,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11725232/pdf/","citationCount":"0","resultStr":"{\"title\":\"Exploring Self-Management Behavior Profiles in Patients with Multimorbidity: A Sequential, Explanatory Mixed-Methods Study.\",\"authors\":\"Yujia Fu, Jingjie Wu, Zhiting Guo, Yajun Shi, Binyu Zhao, Jianing Yu, Dandan Chen, Qiwei Wu, Erxu Xue, Haoyang Du, Huafang Zhang, Jing Shao\",\"doi\":\"10.2147/CIA.S488890\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>This study aims to identify self-management behavior profiles in multimorbid patients, and explore how workload, capacity, and their interactions influence these profiles.</p><p><strong>Patients and methods: </strong>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.</p><p><strong>Results: </strong>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 (<i>OR</i> > 1, <i>P</i> < 0.05) and lower capacity (<i>OR</i> < 1, <i>P</i> < 0.05). An interaction of the overall workload and capacity showed a synergistic effect in the Passive-engagement Profile (<i>OR</i> = 1.08, 95% <i>CI</i> = 1.03-1.13, <i>P</i> < 0.05). Qualitative analysis identified six workload themes, and related coping strategies of three self-management behavior profiles. 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引用次数: 0
摘要
目的：本研究旨在确定多病患者的自我管理行为特征，并探讨工作量、能力及其相互作用如何影响这些特征。患者和方法：采用顺序解释混合方法设计。在定量阶段（2022年8月至2023年5月），收集了来自浙江省9个医疗机构的1,920名多病患者的数据。潜在特征分析（LPA）用于识别不同的自我管理行为特征。然后使用多项逻辑回归来评估工作负载和能力维度（模型1中的自变量）以及它们的相互作用（模型2中的自变量）对这些概况（两个模型中的因变量）的影响。定性阶段（2023年5月至8月）包括16名参与者的半结构化访谈，采用Giorgi分析法对数据进行分类和编码。结果：定量分析显示了三种自我管理行为特征：症状驱动型（8.0%）、被动参与型（29.5%）和主动合作型（62.5%）。与主动合作量表相比，症状驱动量表和被动参与量表均与较高的工作负荷（OR < 1, P < 0.05）和较低的工作能力（OR < 1, P < 0.05）相关。总体工作量和工作能力的相互作用在被动参与中显示出协同效应（OR = 1.08, 95% CI = 1.03-1.13, P < 0.05）。定性分析确定了六个工作量主题，以及三种自我管理行为特征的相关应对策略。综合结果突出了明显的特征：症状驱动型患者表现出反应性行为，健康意识有限，被动参与型患者在症状稳定后会减少参与，而主动合作型患者会主动管理自己的病情。结论：确定三种不同的自我管理行为特征及其与工作量和能力的关系，为了解多病患者的经历提供了有价值的见解，强调需要针对工作量和能力进行量身定制的干预措施，以改善健康结果。
Exploring Self-Management Behavior Profiles in Patients with Multimorbidity: A Sequential, Explanatory Mixed-Methods Study.
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 (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.
期刊介绍：
Clinical Interventions in Aging, is an online, peer reviewed, open access journal focusing on concise rapid reporting of original research and reviews in aging. Special attention will be given to papers reporting on actual or potential clinical applications leading to improved prevention or treatment of disease or a greater understanding of pathological processes that result from maladaptive changes in the body associated with aging. This journal is directed at a wide array of scientists, engineers, pharmacists, pharmacologists and clinical specialists wishing to maintain an up to date knowledge of this exciting and emerging field.
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