Chronic diseases and conditions (e.g., arthritis, diabetes, heart disease, injury-based disabilities) affect more than one-third of adults 65 or older, one-fifth of the population age 60 and older (e.g., 10 million people have diabetes), and one-seventh of the Americans who die annually. The need for self-management programs that enable patients to learn the disease characteristics, practice healthy behaviors, and handle chronic disorders is critical. This research will focus on the complexity of self-management by focusing on participant-centered outcomes, modifications of existing and development of innovative strategies for health self-management through interaction with healthcare providers and reinforcement of positive outcomes. Building on our significant prior experience in developing techniques for management, processing, analysis and visualization of complex multidimensional dataset, the Methods and Analytics Core (MACORE) of the Complexity of Self-management in Chronic Disuse (CSCD) Center will provide the necessary infrastructure and resources to support engagement of patients, family, caregivers, researchers and stakeholders. Specifically, MACORE will support CSCD research projects (pilots, collaborations and services) with data collection and aggregation, formulation and testing of clinically-relevant research hypotheses (across space, time, disease state, phenotypes and treatment regiments), and algorithms to elicit the intricate relations, complex associations, causal connections and complex patterns in the self-management data. The MACORE specific aims are designed to support the broader CSCD-wide Center aims by leveraging complexity to advance the science of self-management for the promotion of health in chronic illness. We will explore new strategies for mentorship by interdisciplinary teams, utilizing innovative methods for analyzing the effects of complex interventions and facilitating development of symposia focused on complex methodology. MACORE will link novice investigators with resources to facilitate development as independent researchers and to lead interdisciplinary teams and develop a range of techniques and supporting documentation for selecting appropriate methods to address complexity for self-management study designs, data processing, and analytical protocols. Finally, the Core will provide methodological and analytic expertise and tools to collaborators and pilot project investigators and develop the CSCD computational infrastructure enabling efficient and reliable end-to-end computational workflow solutions for advanced data analytics.

National Institute of Health (NIH)
National Institute of Nursing Research (NINR)
Exploratory Grants (P20)
Project #
Application #
Study Section
Special Emphasis Panel (ZNR1-REV-M (17))
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Michigan Ann Arbor
Ann Arbor
United States
Zip Code
Costa, Deena Kelly; Moss, Marc (2018) The Cost of Caring: Emotion, Burnout, and Psychological Distress in Critical Care Clinicians. Ann Am Thorac Soc 15:787-790
Kalinin, Alexandr A; Higgins, Gerald A; Reamaroon, Narathip et al. (2018) Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics 19:629-650
Marino, Simeone; Xu, Jiachen; Zhao, Yi et al. (2018) Controlled feature selection and compressive big data analytics: Applications to biomedical and health studies. PLoS One 13:e0202674
Casida, Jesus M; Aikens, James E; Craddock, Heidi et al. (2018) Development and Feasibility of Self-Management Application in Left-Ventricular Assist Devices. ASAIO J 64:159-167
Dinov, Ivo D; Palanimalai, Selvam; Khare, Ashwini et al. (2018) Randomization-Based Statistical Inference: A Resampling and Simulation Infrastructure. Teach Stat 40:64-73
Kalinin, Alexandr A; Allyn-Feuer, Ari; Ade, Alex et al. (2018) 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification. Sci Rep 8:13658
Sepehrband, Farshid; Lynch, Kirsten M; Cabeen, Ryan P et al. (2018) Neuroanatomical morphometric characterization of sex differences in youth using statistical learning. Neuroimage 172:217-227
Tang, Ming; Gao, Chao; Goutman, Stephen A et al. (2018) Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering. Neuroinformatics :
Stelmokas, Julija; Yassay, Lance; Giordani, Bruno et al. (2017) Translational MRI Volumetry with NeuroQuant: Effects of Version and Normative Data on Relationships with Memory Performance in Healthy Older Adults and Patients with Mild Cognitive Impairment. J Alzheimers Dis 60:1499-1510
Huang, Zhengnan; Zhang, Hongjiu; Boss, Jonathan et al. (2017) Complete hazard ranking to analyze right-censored data: An ALS survival study. PLoS Comput Biol 13:e1005887

Showing the most recent 10 out of 25 publications