Research Scientist, MIT Media Lab
Sleep, stress and mental health have been major health issues in modern society. Poor sleep habits and high stress, as well as reactions to stressors and sleep habits, can depend on many factors. Internal factors include personality types and physiological factors and external factors include behavioral, environmental and social factors. What if 24/7 rich data from mobile devices could identify which factors influence your bad sleep or stress problem and provide personalized early warnings to help you change behaviors, before sliding from a good to a bad health condition such as depression? This seminar will present a series of studies and systems we havedeveloped at MIT to investigate how to leverage multi-modal data from mobile/wearable devices to measure, understand and improve mental wellbeing. This seminar will explore the methodology and tools developed for the SNAPSHOT study, which seeks to measure Sleep, Networks, Affect, Performance, Stress, and Health using Objective Techniques. To learn about behaviors and traits that impact health and wellbeing, we have measured over 200,000 hours of multi-sensor and smartphone use data as well as trait data such as personality from about 300 college students exposed to sleep deprivation and high stress. This seminar will also describe statistical analysis and machine-learning models used to characterize, model, and forecast mental wellbeing using the SNAPSHOT study data. We will discuss behavioral and physiological markers and models that may provide early detection of a changing mental health condition. Additionally, this seminar will be introduce you to recent projects that might help people to reflect on, and change, their behaviors with the goal of improving their wellbeing. The seminar will conclude by presenting Ms. Sano’s research vision and future directions in measuring, understanding and improving mental wellbeing.