目的 构建老年冠状动脉硬化性心脏病（以下简称冠心病）患者数字健康技术焦虑及影响因素的混合网络，探究技术焦虑的核心特征，厘清影响因素与技术焦虑的关系，为后续干预提供理论依据和指导。方法 2023年12月至2024年5月，采用便利抽样法选取青岛市4个社区的老年冠心病患者396例为研究对象，运用技术焦虑量表（technophobia scale，TAS）、简版自我感知老化量表（brief ageing perceptions questionnaire，B-APQ）、社会支持量表（social support rating scale，SSRS）、电子健康素养量表（eHealth literacy scale，eHEALS）和数字健康技术自我效能量表（healthcare technology self-efficacy scale，HTSES）等对其进行调查。运用R语言构建网络，计算中心性指标及其稳健性。结果 老年冠心病患者TAS、B-APQ、SSRS、eHEALS、HTSES得分分别为(38.03±11.90)分、（54.09±7.32）分、（36.40±7.87）分、16.00(8.00，26.75)分和（33.08±11.3）分。网络分析显示，“担忧隐私泄露”是技术焦虑网络中预期影响系数最高的节点，“积极结果” “客观支持” “数字健康技术” “网络健康信息与服务的应用能力”为连接各变量与技术焦虑的桥梁节点。结论 医护人员应从技术焦虑网络核心特征及各桥梁节点入手，从而更加精准有效地开展技术焦虑的预防与干预工作。

Objective To develop a mixed network of digital health technophobia and influencing factors in elderly patients with coronary atherosclerotic heart disease (hereinafter referred to as coronary heart disease)，explore the core characteristics of technophobia，clarify the relationship between influencing factors and technophobia，and to provide theoretical basis and guidance for subsequent intervention.Methods From December 2023 to May 2024，396 elderly patients with coronary heart disease from 4 communities in Qingdao were selected by the convenience sampling method，and a survey was conducted with the technophobia scale (TAS)，brief ageing perceptions questionnaire (B-APQ)，social support rating scale (SSRS)，eHealth literacy scale (eHEALS) and the healthcare technology self-efficacy scale (HTSES)，etc.The network was constructed using R language to calculate the centrality index and its robustness.Results The scores of TAS，B-APQ，SSRS，eHEALS and HTSES in elderly patients with coronary heart disease were (38.03±11.90)，(54.09±7.32)，(36.40±7.87)，16.00(8.00，26.75)and (33.08±11.3)，respectively.Network analysis showed that “worry about privacy leakage” was the node with the highest expected impact coefficient in the technophobia network，while “positive outcomes”，“objective support”，“digital health technology”，and “application capabilities of network health information and services” were the bridge nodes connecting each variable with technophobia.Conclusions Medical staff should start from the core characteristics of the technophobia network and each bridge node，so as to carry out the prevention and intervention work of technophobia more precisely and effectively.

R47;R473.59

山东省自然科学基金面上项目(ZR2023MG071)；山东省自然科学基金青年基金(ZR2023QG027)；老年长期照护教育部重点实验室(海军军医大学)