Background: Although robots have emerged as a new means of delivering health information, with the advancement of artificial intelligence technology, individuals still face challenges in deciding whether to trust the health information provided by these robots owing to various trust-related factors.

Objective: This study aimed to investigate the factors associated with the level of trust in health information robots among the general population in China from a socioecological model perspective and identify the central indicators based on network analysis.

Methods: A nationwide survey in China was conducted from June 20, 2023, to August 31, 2023, involving 30,054 participants. The level of trust in health information robots was measured using a self-developed questionnaire. Univariate and multivariate generalized linear model analyses were conducted to investigate the factors associated with the level of trust in health information robots. Network analyses were conducted to examine the network structure of trust levels in health information robots and the associated factors.

Results: The results of the multivariate generalized linear model analysis revealed that participants who were diagnosed with chronic diseases; exhibited personality traits of higher agreeableness and openness; had an education level of junior college or higher; reported higher self-rated health status, health literacy, anxiety symptoms, family health, number of house properties, average monthly household income, and perceived social support; and had higher medical insurance coverage showed a positive association with the level of trust in health information robots compared to individuals without these characteristics. However, compared to individuals without these characteristics, being older, having the personality trait of neuroticism, and living in an urban area were negatively associated with the level of trust in health information robots. In addition, using a network approach, central indicators were identified in the network of the level of trust in health information robots and its associated factors, including family health and perceived social support. Finally, agreeableness and educational level appeared upstream of the entire directed acyclic graph, directly influencing the level of trust in health information robots.

Conclusions: Our findings offer a novel perspective on the association between health information robots and trust and contribute to the application and development of artificial intelligence IT. Individuals' acceptance of and adherence to health information may be enhanced if the factors associated with the level of trust in health information robots are considered.

Keywords: artificial intelligence; health information; network analysis; robots; socioecological model.

©Jiukai Zhao, Yuqi Yang, Juanxia Miao, Xue Wang, Dianjun Qi, Shuang Zang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.06.2025.

背景： 尽管机器人已成为提供健康信息的新手段，随着人工智能技术的发展，由于各种与信任相关的因素，个人在决定是否相信这些机器人提供的健康信息时仍面临挑战。

目的： 本研究旨在从社会生态模型的角度调查中国一般人群中对健康信息机器人的信任程度相关因素，并通过网络分析确定核心指标。

方法： 2023年6月20日至8月31日期间，对中国进行了全国范围内的调查，涉及30,054名参与者。使用自编问卷测量了对健康信息机器人的信任程度。通过单变量和多变量广义线性模型分析来研究与健康信息机器人信任水平相关的因素，并进行网络分析以考察健康信息机器人信任水平及其相关因素的网络结构。

结果： 多变量广义线性模型分析结果显示，被诊断患有慢性病；表现出更高随和性和开放性的个性特征；受教育程度达到大专及以上；报告更高的自我评价健康状况、健康素养、焦虑症状、家庭健康、房产数量、平均每月家庭收入和感知到的社会支持；以及医疗保障覆盖范围更广的参与者，在对健康信息机器人的信任水平方面，与没有这些特性的个人相比有正向关联。然而，相比于不具备这些特征的人群，年龄较大、具有神经质个性特质、居住在城市地区则会负向影响对健康信息机器人信任水平。此外，通过网络方法确定了健康信息机器人信任水平及其相关因素的网络中的核心指标，包括家庭健康和感知到的社会支持。最后，在整个有向无环图中，随和性和受教育程度出现在上游，直接影晌健康信息机器人的信任水平。

结论： 我们的研究结果为健康信息机器人与信任之间的关联提供了新颖的视角，并有助于人工智能信息技术的应用和发展。如果考虑影响对健康信息机器人信任水平的因素，可能会增强个人接受和遵守健康信息的程度。

关键词： 人工智能；健康信息；网络分析；机器人；社会生态模型。

©Jiukai Zhao, Yuqi Yang, Juanxia Miao, Xue Wang, Dianjun Qi, Shuang Zang. 原文发表于《医学互联网研究杂志》（https://www.jmir.org），2025年6月13日。