Network analysis of distress, symptom burden, social support, and digital health literacy in older postoperative patients with gastric cancer

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Aim: To use network analysis to explore the relationships among distress, symptom burden, social support, and digital health literacy in older patients with gastric cancer following surgery. Background: Digital healthcare is gaining increasing prominence and represents a promising approach to improving long-term care and psychological support for older postoperative patients with gastric cancer. However, this population frequently experiences high distress, heavy symptom burden, limited social support, and low DHL, which together constitute major barriers to the effective use of digital health resources. To date, the mechanisms underlying the interactions among these factors remain poorly understood. Therefore, this study seeks to clarify these relationships and provide empirical evidence to support the integration of digital health into geriatric oncology care. Methods: A cross-sectional study was conducted involving 767 older postoperative patients with gastric cancer at a teaching hospital between August 2024 and June 2025. Participants completed validated questionnaires, including the Brief Symptom Inventory-18 (BSI-18), the M. D. Anderson Symptom Inventory Gastrointestinal Cancer Module (MDASI-GI), the Multidimensional Scale of Perceived Social Support (MSPSS), and the eHealth Literacy Scale (e-HEALS). R software version 4.2.1 was used. Network analysis assessed the structure, centrality, stability, and accuracy of these factors. Results: Network analysis revealed that "Blue" and "Tense" of the BSI-18, and "Evaluate" of the e-HEALS were the most central nodes. "Friends" and "Sleep" acted as key bridge nodes, linking distress, symptom burden, and social support domains. The network proved stable and accurate. Conclusions: This study highlighted the item "Blue" as a central node and "Sleep" as a key bridge node. These findings suggest the potential utility of network analysis in precision nursing. Furthermore, measures such as emotional counseling for low mood, sleep optimization, and peer-navigated digital empowerment may help address the interconnected symptom pattern observed in this population.

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