Xiao Li: Finding the Signal in Rich Self-Collected Data
Xiao Li is a lead author on the recent paper “Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information.” In this talk, she describes how starting with data from a single individual person can expose previously unnoticed phenomena, showing evidence that outlier heart rate and skin temperatures can predict sickness onset, sometimes over 10 days in advance, and that heart rate differences can distinguish between insulin sensitive and insulin resistant states.
Highlights from the QS Symposium 2018
Introduction to the Quantified Self Symposium 2018
Reza Mirza: The History and Future of Single-Subject Science
Hugo Campos: 10 Years With An Implantable Cardiac Device, Still No Data Access
Jana Beck: Carb Intake and 60 Lipid Measurements
Azure Grant: Lessons from Blood Testers, a Participant-Led Project
Dorothy D. Sears: Circadian Rhythms and Cardiometabolic Health
Carsten Skarke: Characterizing the Chronobiome with “Supertrackers”
Whitney E. Boesel: Cholesterol Variability Across Postpartum Menstrual Cycles
Xiao Li: Finding the Signal in Rich Self-Collected Data
Jeffrey Olgin: Data Aggregation for N-of-1 to “N-of-Many-Ones”
Dana Lewis: Social Infrastructure for Participant-Led Research
Camille Nebeker: Informed Consent, Self-Consent
Steven Steinhubl: Where “All of Us” Meets All of Us
Sunita Vohra: What N-of-1 Can Do
