POPULATION ANALYSIS OF MORTALITY RISK: PREDICTIVE MODELS USING MOTION SENSORS FOR 100,000 PARTICIPANTS IN THE UK BIOBANK NATIONAL COHORT

Abstract

ABSTRACTWe have developed novel technology for health monitoring, which inputs motion sensors to predictive models of health status. We have validated these models in clinical experiments with carried smartphones, using only their embedded accelerometers. Using smartphones as passive monitors for population measurement is critically important for health equity, since they are ubiquitous in high-income countries already and will become ubiquitous in low-income countries in the near future. Our study simulates smartphones by using accelerometers as sensor input.We analyzed 100,000 participants in UK Biobank who wore activity monitors with motion sensors for 1 week. This national cohort is demographically representative of the UK population, and this dataset represents the largest such available sensor record. We performed population analysis using walking intensity, with participants whose motion during normal activities included daily living equivalent of timed walk tests. We extract continuous features from sensor data, for input to survival analysis for predictive models of mortality risk.Simulating population monitoring, we validated predictive models using only sensors and demographics. This resulted in C-index of 0.76 for 1-year risk decreasing to 0.73 for 5-year. A minimum set of sensor features achieves similar C-index with 0.72 for 5-year risk, which is similar accuracy to previous studies using methods not achievable with phone sensors. The minimum model uses average acceleration, which has predictive value independent of demographics of age and sex, as does the physical measure of gait speed. Our digital health methods achieve the same accuracy as activity monitors measuring total activity, despite using only walking sessions as sensor input, orders of magnitude less than existing methods.AUTHOR SUMMARYSupporting healthcare infrastructure requires screening national populations with passive monitors. That is, looking for health problems without intruding into daily living. Digital health offers potential solutions if sensor devices of adequate accuracy for predictive models are already widely deployed. The only such current devices are cheap phones, smartphone devices with embedded sensors. This limits the measures to motion sensors when the phones are carried during normal activities. So measuring walking intensity is possible, but total activity is not.Our study simulates smartphone sensors to predict mortality risk in the largest national cohort with sensor records, the demographically representative UK Biobank. Death is the most definite outcome, accurate death records are available for 100,000 participants who wore sensor devices some five years ago. We analyzed this dataset to extract walking sessions during daily living, then used these to predict mortality risk. The accuracy achieved was similar to activity monitors measuring total activity and even to physical measures such as gait speed during observed walks. Our scalable methods offer a potential pathway towards national screening for health status.