Network Analysis of Multidimensional Interactions Between Self-Regulatory Fatigue, Decision Conflict, and Quality of Life in Advanced Cancer Patients: Identifying Core Nodes for Precision Intervention

TL;DR

This study uses network analysis to understand how self-regulatory fatigue, decision conflict, and quality of life interact in advanced cancer patients, identifying key targets for precision interventions.

Contribution

The study introduces a new 'node-targeted intervention' model by identifying core, bridge, and negative nodes in a multidimensional network of patient experiences.

Findings

Emotional Function (F3) and Emotional Fatigue (SF2) form a core emotional cluster in the network.

Uncertainty (D1) is identified as the key cognitive hub, while Functional Status (F4) has the highest Expected Influence.

Effective Decision-Making (D3) is a negative key node associated with adverse network effects.

Abstract

Objective: To address the heavy burden of ego depletion and decision conflict in patients with advanced cancer, this study employed network analysis to explore their interaction mechanisms and identify key intervention targets, overcoming the limitations of traditional linear studies. Methods: A total of 200 patients with advanced cancer were assessed using the Self-Regulatory Fatigue Scale (SRFS), Decisional Conflict Scale (DCS), and Functional Assessment of Cancer Therapy-General (FACT-G). A Gaussian Graphical Model (GGM) was constructed to identify key nodes. Results: Network analysis revealed a tight interactive network among ego depletion, decision conflict, and quality of life. Emotional Function (F3) and Emotional Fatigue (SF2) formed a core emotional cluster, while Uncertainty (D1) was the key cognitive hub. The core nodes F3, D1, and Social/Family Function (F2) were identified as crucial regulators connecting different modules. The core node with the highest Expected Influence was F4 (Functional Status, EI = 0.523), and the key bridge node connecting different modules was F2 (Social/Family Function, bridge strength = 1.114). D3 (Effective Decision-Making, EI = −0.469) was identified as a negative key node associated with adverse network effects. Quantitatively, the core nodes of the network were F4 (Functional Status, EI = 0.523), SF3 (Behavioral Fatigue, EI = 0.353), and SF1 (Cognitive Fatigue, EI = 0.326); the bridge nodes were F2 (Social/Family Function, bridge strength = 1.114), SF2 (Emotional Fatigue, bridge strength = 0.966), and D1 (Uncertainty, bridge strength = 0.858); and D3 (Effective Decision-Making, EI = −0.469) was the negative key node. Conclusions: This study challenges the traditional “symptom-specific treatment” model and proposes a new paradigm of “node-targeted intervention.” Qualitatively, this study clarifies the multidimensional interactive mechanism of ego depletion, decision conflict, and quality of life in advanced cancer patients, and identifies key intervention nodes with different functional attributes (core nodes, bridge nodes, negative nodes). It provides empirical evidence for developing targeted palliative care strategies, which may offer new insights for optimizing symptom management in this population. Clinical Relevance: This study highlights the importance of exploring the multidimensional interaction mechanisms between self-regulatory fatigue, decision conflict, and quality of life in advanced cancer patients, emphasizing the guiding role of core nodes (Functional Status, Behavioral Fatigue, Cognitive Fatigue), bridge nodes (Social/Family Function, Emotional Fatigue, Uncertainty), and the negative node (Effective Decision-Making) in precise intervention. The findings support the integration of node-targeted hierarchical interventions into routine palliative care for advanced cancer patients to break the symptom vicious cycle and enhance their quality of life.

Figures