Graduate Trainee, University of Texas at Austin
1 active project
PGHD in Heart Failure Self-Care
Scientific Questions Being Studied
Data created through personal devices can be immense and difficult to sift through, however digital phenotyping, the quantification of the individual phenotype through the use of personal digital devices, in combination with machine learning has the ability to make sense of the data and identify risks for decompensation, transforming and personalizing care (Vaidyam, Halamka, & Torous). This concept is promising; however, the area has not been fully developed, remaining more of a research topic than a clinical tool (Vaidyam, Halamka, & Torous). Utilization of PGHD can increase self-care maintenance and adherence to treatment regimens (Cajita et al., Kiyarosta et al.; Son et al.,). Gaining a better understanding of the data, or digital phenotype will provide more insight into self-management behaviors and improved outcomes. The research question identified is: How does the use of PGHD in heart failure patients, such as activity trackers, predict utilization of healthcare services?
- Disease Focused Research (congestive heart failure)
- Population Health
This is a developing dissertation plan:
Initially the idea is to conduct a quantitative analysis of fitbit data and EHR data to develop a digital phenotype of the HF patient, evaluating if their fitbit use interacts with health are utilizing.
Identifying a digital phenotype of the data will create a better understanding of health behaviors to achieve personalized care. Integrating personal devices to track health information directly links to precision health interventions. Precision health is the future of chronic disease management and patient generated health will have an important role in this development.
The results will inform my dissertation.
Demographic Categories of Interest
- Katelyn Leggio - Graduate Trainee, University of Texas at Austin
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