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Workload-capacity imbalances and their impact on self-management complexity in patients with multimorbidity: a

Binyu Zhao1,2, Yujia Fu1,2, Jingjie Wu3

  • 1Department of Nursing, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, China.

Annals of Medicine
|January 17, 2025
PubMed

Insights

Multimorbidity requires effective self-management. The Cumulative Complexity Model (CuCoM) was validated, showing that higher capacity and lower workload predict better self-management in patients with multiple chronic conditions.

Area of Science:

  • Health Services Research
  • Chronic Disease Management
  • Patient Self-Management

Background:

  • Global increase in multimorbidity necessitates enhanced self-management strategies.
  • The Cumulative Complexity Model (CuCoM) provides a framework for understanding self-management through workload and capacity.
  • Validating CuCoM in multimorbid populations is crucial for developing targeted interventions.

Purpose of the Study:

  • To validate the Cumulative Complexity Model (CuCoM) in a large cohort of multimorbid patients.
  • To identify specific predictors of self-management tailored to different workload and capacity profiles.
  • To inform the development of personalized self-management support for individuals with multiple chronic conditions.

Main Methods:

  • A multicenter, cross-sectional survey of 1920 multimorbid patients in China.
  • Assessment of workload (medication, appointments, life disruption, health issues) and capacity (social, environmental, financial, physical, psychological).
  • Latent profile analysis, regression, and network analysis were used to examine relationships between workload, capacity, and self-management.

Main Results:

  • Four patient profiles emerged: low workload-low capacity (10.2%), high workload-low capacity (7.5%), low workload-high capacity (64.6%), and high workload-high capacity (17.7%).
  • Higher capacity and lower workload were significantly associated with better self-management (β=0.271, p<0.001). Conversely, high workload and low capacity predicted poorer self-management (β=-0.187, p<0.001).
  • Social capacity was the strongest predictor across all profiles, with other capacities (environmental, financial, psychological) showing varying importance depending on the profile. Socioeconomic factors were central in network analysis.

Conclusions:

  • Personalized interventions targeting capacity enhancement and workload reduction are vital for improving self-management in multimorbid patients.
  • Addressing health inequities through upstream policies can further support better self-management outcomes.
  • The CuCoM framework effectively explains self-management variations, guiding tailored clinical and policy approaches.
Abstract

Keywords:
Workloadcapacitycumulative complexity modelmultimorbidityself-management

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