OBJECTIVE Poor lifestyle represents a health risk factor and is the leading cause of morbidity and chronic conditions. The impact of poor lifestyle can be significantly altered by individual's behavioral modification. Although there are abundant lifestyle promotion applications and tools, they are still limited in providing tailored social support that goes beyond their predefined functionalities. In addition, virtual coaching approaches are still unable to handle user emotional needs. Our approach presents a human-virtual agent mediated system that leverages the conversational agent to handle menial caregiver's works by engaging users (e.g., patients) in a conversation with the conversational agent. The dialog used a natural conversation to interact with users, delivered by the conversational agent and handled with a finite state machine automaton. Our research differs from existing approaches that replace a human coach with a fully automated assistant on user support. The methodology allows users to interact with the technology and access health-related interventions. To assist physicians, the conversational agent gives weighting to user's adherence, based on prior defined conditions. MATERIALS AND METHODS This article describes the design and validation of CoachAI, a conversational agent-assisted health coaching system to support health intervention delivery to individuals or groups. CoachAI instantiates a text-based health care conversational agent system that bridges the remote human coach and the users. RESULTS We will discuss our approach and highlight the outcome of a 1-month validation study on physical activity, healthy diet, and stress coping. The study validates technology aspects of our human-virtual agent mediated health coaching system. We present the intervention settings and findings from the study. In addition, we present some user-experience validation results gathered during or after the experimentation. CONCLUSIONS The study provided a set of dimensions when building a human-conversational agent powered health intervention tool. The results provided interesting insights when using human-conversational agent mediated approach in health coaching systems. The findings revealed that users who were highly engaged were also more adherent to conversational-agent activities. This research made key contributions to the literature on techniques in providing social, yet tailored health coaching support: (1) identifying habitual patterns to understand user preferences; (2) the role of a conversational agent in delivering health promoting microactivities; (3) building the technology while adhering to individuals' daily messaging routine; and (4) a socio-technical system that fits with the role of conversational agent as an assistive component. FUTURE WORK Future improvements will consider building the activity recommender based on users' interaction data and integrating users' dietary pattern and emotional wellbeing into the initial user clustering by leveraging information and communication technology approaches (e.g., machine learning). We will integrate a sentiment analysis capability to gather further data about individuals and report these data to the caregiver.

中文翻译：

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健康教练辅助会话代理：一项验证研究。

目的不良的生活方式代表健康风险因素，并且是发病率和慢性病的主要原因。不良的生活方式可能会因个人的行为改变而大大改变。尽管有大量的生活方式推广应用程序和工具，但是它们在提供超出其预定义功能的量身定制的社会支持方面仍然受到限制。此外，虚拟教练方法仍然无法满足用户的情感需求。我们的方法提出了一种人-虚拟代理介导的系统，该系统通过使用户（例如，患者）参与与对话代理的对话，从而利用对话代理来处理轻度护理者的工作。该对话框使用自然对话与用户互动，由对话代理传递并由有限状态机自动机处理。我们的研究与现有的方法不同，现有的方法是将用户教练替换为用户支持的全自动助手。该方法允许用户与该技术进行交互并访问与健康相关的干预措施。为了帮助医生，会话代理根据事先定义的条件对用户的依从性进行加权。材料和方法本文介绍了CoachAI的设计和验证，CoachAI是一种对话式代理辅助的健康指导系统，用于支持向个人或团体提供健康干预。CoachAI实例化了基于文本的医疗保健会话代理系统，该系统将远程人员教练和用户联系在一起。结果我们将讨论我们的方法，并重点介绍一项为期1个月的有关体育锻炼，健康饮食和压力应对的验证研究的结果。该研究验证了我们的人-虚拟代理人介导的健康指导系统的技术方面。我们介绍了这项研究的干预设置和发现。此外，我们介绍了在实验过程中或实验之后收集的一些用户体验验证结果。结论本研究在构建由人类对话代理驱动的健康干预工具时提供了一系列维度。当在健康教练系统中使用人类对话代理介导的方法时，结果提供了有趣的见解。调查结果表明，参与度很高的用户也更喜欢对话代理活动。这项研究对提供社会性，量身定制的健康指导支持技术的文献做出了重要贡献：（1）识别习惯模式以了解用户偏好；（2）对话剂在提供促进健康的微活动中的作用；（3）在遵守个人日常通讯程序的同时建立技术；（4）符合会话代理作为辅助组件作用的社会技术系统。未来的工作未来的改进将考虑基于用户的交互数据构建活动推荐器，并通过利用信息和通信技术方法（例如，机器学习）将用户的饮食习惯和情绪健康整合到初始用户集群中。