A mobile data analysis framework for environmental health decision support

Relations between negative health effects like asthma and lung cancer and elevated levels of the environmental factors, such as air pollution, tobacco smoke and humidity, have been detected in several large scale exposure studies. Thus, public health care and service systems require the ability to track, monitor, store, and analyze individual moving trajectories along with several environmental conditions the individual is exposed to in order to identify meaningful relationships among theses data and derive conclusions for environmental health decision support. With continued advances in information technology, patients can be monitored with numerous intelligent devices. Sensors can be integrated into their mobile devices such as smart phones for continuous health assistance and disease attack prevention. However, researchers must overcome many challenges, such as data acquisition, data scales and data uncertainty, in order to develop a real-time health monitoring system. In this paper, we propose a system framework for modeling and analyzing individual exposure to environmental triggers of asthma attacks. The proposed system can provide a tool to develop more accurate asthma prevention and care plans enabled by real-time patient monitoring and communication through alerts for potential environmental triggers.