Imagine in the future, you are excited about a big day coming up when you have an interview for your dream job opportunity. You select on your smartwatch app that it's important that you are well rested and energized, in a good mood, and able to remember crucial things you prepared for the interview. For the next week the smartwatch will monitor your sleep, behaviors and physiology, and will provide personalized recommendations which will help improve your performance during the interview. This proposal aims to advance the science and practice that would enable the key components of such systems.
Such a future where smart devices support us with optimal performance and well-being is attainable with wearable technology that understands how your sleep contributes to your overall health. Sleep plays a critical role in cognitive performance, mood, and general well-being. The goal of our proposed research is to advance research on sleep monitoring and interventions, and translate the best insights into a suite of algorithms for the Galaxy Watch and phone that users can leverage to improve their performance and wellbeing by optimizing sleep.
One key focus of this project is to develop wearable-based sleep biomarkers that have validated links with mood, fatigue, and cognition, surpassing the simple measures of sleep time or sleep stages currently offered by wearable devices. We aim to develop models for human circadian rhythms based on smartwatch data, which will facilitate personalized fatigue detection and cognitive performance forecasting. Similarly, we will develop an index of “sleep homeostasis” with physiological and behavioral data, identifying whether an individual met their sleep needs. Such analytics will allow us to forecast mood based on “sleep debt” rather than merely the number of hours slept. We will use our previous work in predicting depression from wearables and smartphone data1 to build models that predict fatigue, cognitive performance, and mood based on continuous and passive data collected from Samsung devices.
We also aim to develop and validate interventions to improve sleep, mood, and cognitive performance. We will experiment with a suite of interventions that encourage users to improve their circadian rhythm, homeostasis and sleep quality, whose effectiveness have been established in clinical trials but not implemented on wearable devices. We will study whether these interventions can improve quality of life and general well-being, as reflected in fatigue, cognition, and mood/mood disorder measurements.