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

Abstract

Introduction: Multimorbidity is increasing globally, emphasizing the need for effective self-management strategies. The Cumulative Complexity Model (CuCoM) offers a unique perspective on understanding self-management based on workload and capacity. This study aims to validate the CuCoM in multimorbid patients and identify tailored predictors of self-management.

Methods: This multicenter cross-sectional survey recruited 1920 multimorbid patients in five primary health centres and four hospitals in China. The questionnaire assessed workload (drug intake, doctor visits and follow-up, disruption in life, and health problems), capacity (social, environmental, financial, physical, and psychological), and self-management. Data were analyzed using latent profile analysis, chi-square, multivariate linear regression, and network analysis.

Results: d Patients were classified into four profiles: 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%). Patients with low workload and high capacity exhibited better self-management (β = 0.271, p < 0.001), while those with high workload and low capacity exhibited poorer self-management (β=-0.187, p < 0.001). Social capacity was the strongest predictor for all profiles. Environmental capacity ranked second for 'high workload-high capacity' (R² = 3.26) and 'low workload-low capacity' (R² = 5.32) profiles. Financial capacity followed for the 'low workload-high capacity' profile (R² = 5.40), while psychological capacity was key in the 'high workload-low capacity' profile (R² = 6.40). In the network analysis, socioeconomic factors exhibited the central nodes (p < 0.05).

Conclusions: Personalized interventions designed to increase capacity and reduce workload are essential for improving self-management in multimorbid patients. Upstream policies promoting health equity are also crucial for better self-management outcomes.

Keywords: Workload; capacity; cumulative complexity model; multimorbidity; self-management.

Figure 1.

Relative importance of 17 independent variables on (a) ‘high workload-high capacity’ profile, (b) ‘low workload-low capacity’ profile, (c) ‘low workload-high capacity’ profile, and (d) ‘high workload-low capacity’ profile. Note: the metric of ‘lmg’ (R2 partitioned by averaging over orders) is used to quantify the contribution of the independent variables to the dependent variable. *age, sex, place of residence, educational level, labour market attachment, monthly household income, medical insurance, and Charlson comorbidity index.

Figure 2.

Estimated regularized network structure of (a) ‘high workload-high capacity’ profile, (b) ‘low workload-low capacity’ profile, (c) ‘low workload-high capacity’ profile, and (d) ‘high workload-low capacity’ profile. The edges are colour-coded to indicate partial correlations, with green representing positive and red representing negative. Thicker lines represent stronger connections.

Figure 3.

Proposed self-management reservoir schematic. Self-management (yellow pool) is regarded as a dynamic resource. When patient capacities overwhelm workloads, the pointer will move towards the right, increasing the resource of self-management (red arrow). Note: initial workload-capacity imbalance (capacities > workloads) might allow individuals to invest further, rendering them less vulnerable to loss and loss spirals. When patient workloads overwhelm capacities, the pointer will move towards the left, decreasing the resource of self-management (blue arrow). Initial workload-capacity imbalance (workload > capacities) might bring individuals caught in a loss spiral.