Causally Linking Health Application Data and Personal Information Management Tools

TL;DR

This paper introduces a framework that integrates health device data with personal information tools to visualize and infer causal relationships among diverse health and contextual data streams.

Contribution

It proposes a novel framework combining data integration, analysis, visualization, and inference methods to reveal causal links in health-related data from multiple sources.

Findings

Framework effectively visualizes causal relationships

Enhances understanding of health data interactions

Supports personalized health insights

Abstract

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.