Towards Integrative Multi-Modal Personal Health Navigation Systems: Framework and Application

TL;DR

This paper introduces a comprehensive Personal Health Navigation framework that leverages continuous data streams and personalized models to guide individuals towards optimal health outcomes, demonstrated through cardiology experiments.

Contribution

It presents a novel generalized PHN framework and demonstrates its application in cardiology through clinical trials and large-scale data analysis.

Findings

Successful pilot clinical trial with 41 users.

Effective personalized cardiovascular exercise models from smartwatch data.

Identification of key challenges in health data integration and system deployment.

Abstract

It is well understood that an individual's health trajectory is influenced by choices made in each moment, such as from lifestyle or medical decisions. With the advent of modern sensing technologies, individuals have more data and information about themselves than any other time in history. How can we use this data to make the best decisions to keep the health state optimal? We propose a generalized Personal Health Navigation (PHN) framework. PHN takes individuals towards their personal health goals through a system which perpetually digests data streams, estimates current health status, computes the best route through intermediate states utilizing personal models, and guides the best inputs that carry a user towards their goal. In addition to describing the general framework, we test the PHN system in two experiments within the field of cardiology. First, we prospectively test a knowledge-infused cardiovascular PHN system with a pilot clinical trial of 41 users. Second, we build a data-driven personalized model on cardiovascular exercise response variability on a smartwatch data-set of 33,269 real-world users. We conclude with critical challenges in health computing for PHN systems that require deep future investigation.