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. It will develop and implement a standards-based, interoperable, extensible and open-source big data software platform for efficient implementation of MD2K data analytics. The MD2K Center will demonstrate the feasibility, utility, and generalizability of the MD2K approach by implementing the entire MD2K data analytics system in the context of two biomedical applications - reducing relapse among abstinent daily smokers and reducing readmission among congestive heart failure (CHF) patients. All tools and software developed by the Center will be freely available as standards-compliant and open-source with documentation to engage data science researchers in advancing the science of MD2K and its integration with other biomedical big data such as EHRs, genomics, and imaging. The Center will provide training materials to help biomedical researchers install the MD2K software on their mobile devices and servers to collect mobile sensor data and analyze these data for biomedical discovery. The Administration Core of the MD2K Center will establish an organizational structure, operating procedures, and managerial practices that effectively facilitate coordination, communication and collaboration among team members. It will develop quantifiable measures and implement systems to monitor, assess, and evaluate the quality and utility of MD2K products and training to measure progress and gauge the long term impact of Center activities. It will implement administrative management systems and procedures to facilitate management of the Center's financial, communications and research infrastructure, resources, and networks. It will ensure real-time access to meaningful information, data, and tools among MD2K team members, BD2K Consortium members, NIH, and interested data science and biomedical researchers via robust web tools and social media. Finally, it will explore and implement specific mechanisms for continued maintenance and distribution of software and tools to the research community beyond the funding period.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54EB020404-01
Application #
8906245
Study Section
Special Emphasis Panel (ZRG1-BST-N (52))
Project Start
Project End
Budget Start
2014-09-29
Budget End
2015-05-31
Support Year
1
Fiscal Year
2014
Total Cost
$272,197
Indirect Cost
$78,279
Name
University of Memphis
Department
Type
DUNS #
055688857
City
Memphis
State
TN
Country
United States
Zip Code
38152
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
Saleheen, Nazir; Ali, Amin Ahsan; Hossain, Syed Monowar et al. (2015) puffMarker: A Multi-Sensor Approach for Pinpointing the Timing of First Lapse in Smoking Cessation. Proc ACM Int Conf Ubiquitous Comput 2015:999-1010

Showing the most recent 10 out of 63 publications