Objective:

To explore the relationship among psychoneurological symptom cluster (PNSC) in breast cancer patients and identify potential intervention targets.

Approach:

• Study Design: Cross-sectional study involving 304 breast cancer patients who received treatment.
• Data Collection: Self-report data collected using the Cancer Fatigue Scale, Pittsburgh Sleep Quality Index, Hospital Anxiety and Depression Scale, and Visual Analog Scale for Pain.
• Network Analysis: Static interrelationships examined using Gaussian network model; directional associations explored with Bayesian network analysis.
• Simulated Interventions: Dynamic correspondence between symptoms assessed through computer-simulated interventions.

Key Findings:

• Physical fatigue, sleep latency, and depression identified as core symptoms with expected influence values of 1.883, 0.940, and 0.794, respectively.
• Physical fatigue, pain, and daytime dysfunction recognized as bridging symptoms with bridge expected influence values of 1.824, 1.558, and 1.331, respectively.
• Depression showed the largest reduction in simulated interventions, with sum scores declining from 5.98 to 4.67, followed by affective fatigue and physical fatigue.

Interpretation:

Physical fatigue and depression may be prioritized as intervention targets to disrupt symptom interactions within the PNSC.

Limitations:

• Study conducted within a specific time frame and location, which may limit the generalizability of the findings.
• Further research is needed to verify the effectiveness of targeting identified symptoms.

Conclusion:

Integration of computer-simulated interventions with network analysis provides insights into symptom interactions and potential intervention targets.