Towards Real-Time Public Health: A Novel Mobile Health Monitoring System

Public health monitoring methods have limitations that affect the quality of data. To support traditional data collection efforts, personal smart technologies can be used to collect multimodal, real-time and continuous data. Public health agencies can then study and predict the prevalence of conditions in a population using advanced analytics. Apple Health is one of the most popular sources of health data from personal devices, supporting diverse sensors that collect a wide range of information from heart rate to blood pressure and sleep. This paper introduces a system that uses a mobile health platform to extract Apple Health data to support public health monitoring. Development, security and privacy considerations are discussed, and a pilot study is proposed which collects several objective sensor data from Apple Health as well as self-report perceived stress (both using the platform) to create stress prediction models. Ultimately, the system described can provide public health agencies with novel methods to collect multimodal data from consumer devices as well as implement interventions in real-time to minimize the impact of conditions, such as stress, in a population. The system advances the state-of-the-art in health monitoring by being one of the first works to leverage health data from consumer-level personal devices for public health.