Project 458945

A major barrier in understanding the link between stress and chronic disease is the accurate measurement of stress in real time. The capabilities of digital devices such as smartphones and smart watches make the real time measurement of stress a potential reality. Wearable technologies and smartphone apps are capable of assessing both immediate signs of stress (e.g., physiological metrics such as heart rate, heart rate variability, and blood pressure) and intermediate stress such as shifts in sleep quality, mood, and cognition. These devices, paired with Machine Learning (ML) methods, may reveal insights into the link between stress and chronic disease. Throughout my PhD I aim to: 1) build ML methods to model stress using wearable sensor data, 2) apply these models to a real-world observational study in a population with cancer predisposition and high stress, and 3) understand how these findings can be actionable to both patients and physicians. This work has dramatic opportunities for early intervention in high-risk patients with constant stress-burden as well as other areas of health. I will contribute to a research domain that is leveraging digital tools and ML to reimagine 21st century healthcare