The proliferation of consumer health devices such as smart watches, sleep monitors, smart scales, etc, in many countries, has not only led to growing interest in health monitoring, but also to the development of a countless number of ``smart'' applications to support the exploration of such data by members of the general public, sometimes with integration into professional health services. While a variety of health data streams has been made available by such devices to users, these streams are often presented as separate time-series visualizations, in which the potential relationships between health variables are not explicitly made visible. Furthermore, despite the fact that other aspects of life, such as work and social connectivity, have become increasingly digitised, health and well-being applications make little use of the potentially useful contextual information provided by widely used personal information management tools, such as shared calendar and email systems. This paper presents a framework for the integration of these diverse data sources, analytic and visualization tools, with inference methods and graphical user interfaces to help users by highlighting causal connections among such time-series.

翻译：在多个国家，智能手表、睡眠监测器、智能秤等消费级健康设备的普及，不仅激发了人们对健康监测的日益关注，也催生了无数支持普通公众探索此类数据的“智能”应用程序，部分应用还实现了与专业健康服务的集成。尽管这些设备向用户提供了多种健康数据流，但这些数据流通常以独立的时间序列可视化形式呈现，其中健康变量之间的潜在关系并未明确显现。此外，尽管工作与社交等其他生活领域已日益数字化，健康与福祉类应用却极少利用广泛使用的个人信息管理工具（如共享日历与电子邮件系统）所提供的潜在有用情境信息。本文提出一个集成框架，将多样化数据源、分析与可视化工具、推理方法及图形用户界面相结合，通过突出时间序列间的因果关系，帮助用户理解这些数据。