The ubiquity of smartphones has led to an increase in on demand healthcare being supplied. For example, people can share their illness-related experiences with others similar to themselves, and healthcare experts can offer advice for better treatment and care for remediable, terminal and mental illnesses. As well as this human-to-human communication, there has been an increased use of human-to-computer digital health messaging, such as chatbots. These can prove advantageous as they offer synchronous and anonymous feedback without the need for a human conversational partner. However, there are many subtleties involved in human conversation that a computer agent may not properly exhibit. For example, there are various conversational styles, etiquettes, politeness strategies or empathic responses that need to be chosen appropriately for the conversation. Encouragingly, computers are social actors (CASA) posits that people apply the same social norms to computers as they would do to people. On from this, previous studies have focused on applying conversational strategies to computer agents to make them embody more favourable human characteristics. However, if a computer agent fails in this regard it can lead to negative reactions from users. Therefore, in this dissertation we describe a series of studies we carried out to lead to more effective human-to-computer digital health messaging. In our first study, we use the crowd [...] Our second study investigates the effect of a health chatbot's conversational style [...] In our final study, we investigate the format used by a chatbot when [...] In summary, we have researched how to create more effective digital health interventions starting from generating health messages, to choosing an appropriate formality of messaging, and finally to formatting messages which reference a user's previous utterances.

翻译：智能手机的普及推动了按需医疗服务的增长。例如，人们可以与他人分享自身患病经历，医疗专家也能为可治愈、终末期及精神疾病患者提供更优的治疗与护理建议。在人机交互领域，数字健康信息传递（如聊天机器人）的使用日益增多，其优势在于无需人类对话伙伴即可提供同步匿名反馈。然而，计算机代理可能无法恰当展现人类对话中的诸多微妙之处，例如需要根据对话情境选择合适的会话风格、礼仪、礼貌策略或共情回应。令人鼓舞的是，“计算机是社会行动者”（CASA）理论指出，人们会将对人类的社会规范同样应用于计算机。基于此，先前研究聚焦于将对话策略应用于计算机代理，使其呈现更受欢迎的人类特质。但若计算机代理在此方面失败，可能引发用户的负面反应。因此，本论文描述了一系列旨在提升人机数字健康信息传递效果的研究。在第一项研究中，我们利用众包...第二项研究探讨健康聊天机器人的会话风格影响...最终研究则调查了聊天机器人在引用用户先前话语时的信息格式...总之，我们研究了如何创建更有效的数字健康干预措施：从生成健康信息、选择合适的信息正式程度，到最终格式化引用用户先前话语的信息。