Picturing Patterns in Whole-Person Health: Leveraging Visualization Techniques with Structured Consumer-Generated mHealth Data

University of Minnesota Ph.D. dissertation. December 2018. Major: Nursing. Advisors: Karen Monsen, Ruth Lindquist. 1 computer file (PDF); vi, 92 pages.Cardiovascular disease (CVD) is a leading cause of death in women. In cardiac care-management, women have experienced being seen “as the disease” rather than as a whole person. Current methods are lacking to better understand a whole person perspective to include strengths, challenges, and needs. Health information technology (HIT) holds promise for capturing data that represents the whole-person perspective. A literature review identified that women with cardiovascular disease have strengths and would like their strengths used as part of managing care. A consumer-facing application, MyStrengths+MyHealth app, was developed to enable self-report of strengths, challenges, and needs using a consumer-facing version of the Omaha System, a multi-disciplinary standardized health terminology. The Omaha System problem concept, Circulation, was used as a surrogate for women with cardiovascular disease. Participants (N=604) used the MSMH app at Midwestern state fair and women with Circulation signs/symptoms (n=80) were matched to an equal number of women without Circulation signs/symptoms. Data generated by participants were analyzed using descriptive statistics and data visualizations techniques to evaluate and compare standardized strengths, challenges, and needs for women with Circulation signs/symptoms. This study revealed women with Circulation signs/symptoms had more strengths, challenges, and needs compared to women without Circulation signs/symptoms. Data visualizations techniques detected differing patterns in the data for women with and without Circulation signs/ symptoms. Future research is needed to validate these findings and extend this research to other populations and programs. This research creates a foundation for what is possible using data visualizations to enhance understanding of consumer-generated health data