Rapid advances in technology are leading to field-deployable mobile sensing devices that can be used to quantify complex dynamics of key physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk across time and help us thus break through to the next level of biomedical understanding of causation in complex disorders. While ongoing efforts have focused on and made significant strides in the analysis of "big data" in the areas of genomics, imaging, and EHR, significant new investment is needed to provide training in data analytics tools specific to the unique features of mobile sensor data to enable scientists to convert this wealth of data into information, knowledge, and action. Investment in a strong, open, scientific, and computational infrastructure for mobile "big data" at this early stage promises outsize returns to advance science, but only if accompanied by easily-accessible training and education. The Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K) will generate generalizable theory, methods, tools, and software, to address major barriers to processing complex mobile sensor data and using it for biomedical discovery. The Training Core of the MD2K Center will enable the broader biomedical research community beyond the Center to use the MD2K data analysis tools for biomedical discovery in a range of biomedical applications by providing online tutorials, training videos, virtual seminars and a comprehensive web-based resource library. It will engage the data science research community in advancing the science of MD2K itself by equipping them with data sets, open-source software, documentation, and online forums so they can contribute new theories, software, and data sets, and integrate new sensors and other data types into the MD2K platform. It will develop curricular materials for use in classrooms, in both data science and biomedical disciplines, to provide MD2K training to students, and begin the process of building the next generation of scientific workforce that is capable of using MD2K in increasingly sophisticated biomedical applications.
By enabling the use of mobile sensor data to detect and predict person-specific disease risk factors ahead of the onset of adverse clinical events, this project will make it possible to intervene anytime, anywhere, to anticipate and prevent disease complications at the earliest timepoint, ultimately supporting wellness. These tools, made freely available, will reduce the burden of complex chronic disorders on health and healthcare.
|Gulzar, Muhammad Ali; Interlandi, Matteo; Yoo, Seunghyun et al. (2016) BigDebug: Debugging Primitives for Interactive Big Data Processing in Spark. Proc Int Conf Softw Eng 2016:784-795|
|Liao, Peng; Klasnja, Predrag; Tewari, Ambuj et al. (2016) Sample size calculations for micro-randomized trials in mHealth. Stat Med 35:1944-71|
|Chatterjee, Soujanya; Hovsepian, Karen; Sarker, Hillol et al. (2016) mCrave: Continuous Estimation of Craving During Smoking Cessation. Proc ACM Int Conf Ubiquitous Comput 2016:863-874|
|Poncela-Casasnovas, Julia; Spring, Bonnie; McClary, Daniel et al. (2015) Social embeddedness in an online weight management programme is linked to greater weight loss. J R Soc Interface 12:20140686|
|Sharma, Vinod; Rathman, Lisa D; Small, Roy S et al. (2015) Stratifying patients at the risk of heart failure hospitalization using existing device diagnostic thresholds. Heart Lung 44:129-36|
|Sharmin, Moushumi; Raij, Andrew; Epstien, David et al. (2015) Visualization of Time-Series Sensor Data to Inform the Design of Just-In-Time Adaptive Stress Interventions. Proc ACM Int Conf Ubiquitous Comput 2015:505-516|
|Pienta, Robert; Tamersoy, Acar; Tong, Hanghang et al. (2015) Interactive Querying over Large Network Data: Scalability, Visualization, and Interaction Design. IUI 2015:61-64|
|Botoseneanu, Anda; Ambrosius, Walter T; Beavers, Daniel P et al. (2015) Prevalence of metabolic syndrome and its association with physical capacity, disability, and self-rated health in Lifestyle Interventions and Independence for Elders Study participants. J Am Geriatr Soc 63:222-32|
|Khayat, Rami; Jarjoura, David; Porter, Kyle et al. (2015) Sleep disordered breathing and post-discharge mortality in patients with acute heart failure. Eur Heart J 36:1463-9|
|Krahnke, Jason S; Abraham, William T; Adamson, Philip B et al. (2015) Heart failure and respiratory hospitalizations are reduced in patients with heart failure and chronic obstructive pulmonary disease with the use of an implantable pulmonary artery pressure monitoring device. J Card Fail 21:240-9|
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