BigData 2014 - Big Data Opportunities and Challenges in Mobile Health

The goal of this workshop is to provide an informal forum to discuss important research questions and practical challenges in data mining (wrangling) related to mobile health. This workshop will explore the opportunities for big data in small samples within the developing field of mHealth. Opportunities include big data as it is used within adaptive mHealth interventions, as well as in new computational opportunities arising from mHealth data, possibly linked to the trail of digital data that we leave as part and parcel of our daily lives in the 21st century.
The rise of mobile and wireless technologies targeting health behavior (mHealth) has ushered in a new world of health research. mHealth technologies allow researchers to explore actions in real time and to use a range of methods from electronic records (health and other), passive sensors, cameras and self-report to document actions, the environment and perceptions in real-time. These technologies provide opportunities for measuring and computationally exploring health in ways that were simply impossible before. We can now monitor physical activity, biomarkers, heart rate, blood pressure, indicators of stress, smoking, social interactions, geographical location and a host of other internal, external, personal, social and contextual factors in real time. Much of the observation can now be done through unobtrusive and passive sensing, yielding intensive, longitudinal measurements that are time stamped, transmittable, and immediately available for analysis. These data will support health research and the science of behavior change by providing insight into how multiple feedback loops between moment-to-moment events, states and environments interact in real time to impact health. These data can thus be used to build computational models of human behavior, and, ultimately, human health. These computational models can be used to optimize intervention that can be accomplished in real-time and adaptively, so that better treatment, healthcare delivery systems and prevention programs can be designed.
NIH has a new initiative for behavioral interventions to address multiple chronic health conditions in primary care (PA-12-114). NIH also has funding opportunities for the development of software and analysis methods for biomedical big data in targeted areas of high need (RFA-HG-14-020). Comprehensive, coordinated care of chronic conditions is essential to meeting healthcare needs. This requires health “interoperability” of data among consumers (patients) and relevant healthcare providers at multiple levels. Many challenges remain in the development of informatics tools for mHealth such as signal/data processing, data integration and fusion, smart algorithms, safety, ownership/control of data, robust and secure communication, and information presentation/display.