在处理家族性癌症风险时，寻求基于网络的健康信息可以成为不同利益相关者群体（例如患者、亲属以及疑似存在升高的家族性癌症风险的人）的应对策略。在一个被各种各样的基于网络的信息质量和不断发展的技术所标志的广阔数字景观中，信任成为一个关键因素，指导健康信息寻求和与数字健康服务交互的过程。在健康信息中的信任形成可以被概念化为依赖于特定背景并且具有多维特征的，包括3个关键维度：信息寻求者（信托方）、信息提供者（受托方）以及媒介或平台（应用）。由于数字环境的快速变化，了解信息寻求者在新服务中如何形成信任是至关重要的，考虑到这些不同维度之间的相互作用。这样一个新服务的例子是由医生为公众提供个性化癌症相关信息，并且侧重于家族性癌症风险的在线聊天服务。为了全面了解癌症相关在线聊天服务中的信任形成，本研究基于基于网络健康信息信任模型，调查了信托者、受托者和应用程序这3个信任维度及其相关特征。此外，本研究旨在比较这些特征在3个不同利益相关者群体中的差异，以增强该服务对每个群体的可信度。本定性研究通过采访和焦点小组讨论收集了医学癌症顾问、癌症支持团体顾问和公众成员的不同观点，探讨了信托者、受托者和应用程序这3个信任维度及其决定因素，以评估在德国癌症信息服务中实施的一个面向家族性癌症风险的在线聊天服务。结果表明，对家族性癌症风险的经验是使用和信任在线聊天服务提供的信息的关键信托者特征。在线聊天服务可能特别有价值的对象还包括有未满足的需求与医患之间互动的少数族裔群体。参与者强调信托者的特征，例如能力、仁慈、正直和人性（即非聊天机器人），是一个值得信赖的癌症在线聊天服务中的关键。还发现与应用程序相关的特征，包括机构声誉、用户中心设计、现代技术和视觉吸引力，也被认为是必不可少的。尽管3个利益相关者群体具有不同的背景和社会经济特征，但在3个信任维度及其各自特征中发现了很多重叠之处。癌症信息在线聊天的信任是由不同信任维度和特征构成的。本研究强调了对数字健康服务中信任形成的重要性，并提出了增强在线聊天服务中有效和可信互动的潜在改进措施（例如提供医疗专家的个人简历以区分其与人工智能聊天机器人的区别）。© Hanna Luetke Lanfer、Doreen Reifegerste、Annika Berg、Paula Memenga、Eva Baumann、Winja Weber、Julia Geulen、Anne Müller、Andrea Hahne、Susanne Weg-Remers。原文发表于《医学互联网研究杂志》(https://www.jmir.org)，发布于2023年8月23日。

In dealing with familial cancer risk, seeking web-based health information can be a coping strategy for different stakeholder groups (ie, patients, relatives, and those suspecting an elevated familial cancer risk). In the vast digital landscape marked by a varied quality of web-based information and evolving technologies, trust emerges as a pivotal factor, guiding the process of health information seeking and interacting with digital health services. This trust formation in health information can be conceptualized as context dependent and multidimensional, involving 3 key dimensions: information seeker (trustor), information provider (trustee), and medium or platform (application). Owing to the rapid changes in the digital context, it is critical to understand how seekers form trust in new services, given the interplay among these different dimensions. An example of such a new service is a live chat operated by physicians for the general public with personalized cancer-related information and a focus on familial cancer risk.To gain a comprehensive picture of trust formation in a cancer-related live chat service, this study investigates the 3 dimensions of trust-trustor, trustee, and application-and their respective relevant characteristics based on a model of trust in web-based health information. In addition, the study aims to compare these characteristics across the 3 different stakeholder groups, with the goal to enhance the service's trustworthiness for each group.This qualitative study triangulated the different perspectives of medical cancer advisers, advisers from cancer support groups, and members of the public in interviews and focus group discussions to explore the 3 dimensions of trust-trustor, trustee, and application-and their determinants for a new live chat service for familial cancer risk to be implemented at the German Cancer Information Service.The results indicate that experience with familial cancer risk is the key trustor characteristic to using, and trusting information provided by, the live chat service. The live chat might also be particularly valuable for people from minority groups who have unmet needs from physician-patient interactions. Participants highlighted trustee characteristics such as ability, benevolence, integrity, and humanness (ie, not a chatbot) as pivotal in a trustworthy cancer live chat service. Application-related characteristics, including the reputation of the institution, user-centric design, modern technology, and visual appeal, were also deemed essential. Despite the different backgrounds and sociodemographics of the 3 stakeholder groups, many overlaps were found among the 3 trust dimensions and their respective characteristics.Trust in a live chat for cancer information is formed by different dimensions and characteristics of trust. This study underscores the importance of understanding trust formation in digital health services and suggests potential enhancements for effective, trustworthy interactions in live chat services (eg, by providing biographies of the human medical experts to differentiate them from artificial intelligence chatbots).©Hanna Luetke Lanfer, Doreen Reifegerste, Annika Berg, Paula Memenga, Eva Baumann, Winja Weber, Julia Geulen, Anne Müller, Andrea Hahne, Susanne Weg-Remers. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.08.2023.